I’ve stopped reading them.

Most feel like they were written by someone who spent 20 minutes with each tool. No real workflow behind it. No honest take on what actually stuck.

So here’s mine. These are 5 tools that have genuinely become part of how I work, not tools I’ve tried once or revisit occasionally, but ones I reach for almost every day.

There are other tools in my stack, but these are the ones that have earned a permanent spot. Some I pay for out of my own pocket. Some are covered by my employer.

Let’s get to it…

1. Wispr Flow (personal)

It’s a voice-to-text tool. But calling it that undersells it.

I think much faster when I’m speaking than when I’m typing. So Wispr Flow essentially removed the bottleneck between my brain and everything else in my stack. I use it to brainstorm out loud, give instructions to Claude, write Slack messages, and dump half-formed thoughts I clean up later.

That last one is something I do constantly: speak a messy brain dump into Wispr, hand it to Claude to organize, then use Claude to expand it into documentation, a deeper investigation, or a full draft. It’s become a core part of how I go from idea to output.

Because it sits at the input layer of everything I do, it speeds up every other tool on this list.

If you type everything and feel like your hands can’t keep up with your brain, this is worth trying.

Claude (personal Max, work standard)

Claude is the center of my stack.

It’s how I brainstorm, write and debug code, think through problems, and draft communications. If Wispr Flow is the input layer, Claude is the brain.

My work covers Claude at a Pro tier. I personally pay for Max, not for my day job, but for everything else I build and experiment with outside of it. The difference matters for longer context, heavier reasoning tasks, and Claude Code, which handles end-to-end implementation, catches its own mistakes, and reasons through complex multi-step tasks in a way that genuinely changes what’s possible in a single working session.

Windsurf (personal)

My work covers Cursor. I’ve used it and it’s good.

But I still pay for Windsurf out of pocket. I know that on paper it probably looks redundant: I already have Claude Code. I could just use VS Code and call it a day.

The honest answer is that Windsurf has features I genuinely enjoy using, and the IDE experience feels complete in a way that keeps me coming back. The agentic features are accurate and don’t require constant babysitting. It’s become a reliable backup that I reach for on personal projects, and occasionally I’ll use it alongside Claude in a sort of adversarial mode, where one checks the other’s work. Two different models, two different reasoning paths, one output that’s been pressure-tested.

Is it strictly necessary? Probably not. But it earns its place.

It’s my go-to setup for personal projects, weekend experiments, and anything I’m building outside of work.

“Take Notes for Me”, Google Meet (work)

This one is easy to overlook because it’s baked into Google Meet, not a standalone product.

But “Take Notes for Me” has quietly become one of the most useful things in my workflow.

I work closely with stakeholders. Design discussions, alignment calls, requirements sessions. A lot of important context lives in those conversations. I used to take manual notes and still miss things.

Now I let Gemini handle it, and after the call I have a structured summary, action items, and a full transcript I can actually work with. More importantly, I can feed that directly as context into Claude and other tools. The discussion becomes usable input, not a memory I’m trying to reconstruct.

Not flashy. But it quietly makes everything else work better.

📚 NotebookLM

NotebookLM isn’t in my stack every single day, but it shows up often enough, and for a specific enough reason, that it belongs here.

When I’m doing research (reading papers, going deep on a topic) I don’t fully trust any single source, including Claude. Claude is excellent, but it can hallucinate, especially on niche or recent material.

NotebookLM lets me feed in the actual papers and articles I’m working with and get explanations and outlines grounded in those sources. It’s my verification layer. When I need to trust the answer, not just get one, this is where I go.

How it all fits together

Listing tools makes them sound more isolated than they are. In practice, this is one system.

• Wispr Flow handles the input, speaking is faster than typing, so it removes friction at the start of almost every task.

• Claude is where the real thinking and overhaul happens.

• Windsurf gives me an IDE I enjoy working in, with Claude as a pressure-test on its output.

• Google Workspace transcription captures stakeholder context that would otherwise get lost and feeds it downstream.

• NotebookLM closes the loop when I need sourced answers rather than fast ones.

Each tool has a job. Together, they cover the full arc from raw idea to finished output.

A couple of other great resources:

• 🚀 Ready to take the next step? Get my free workshop on agentic analytics for data scientists.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope you find this dashboard useful.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

That question has been driving a lot of my research over the past few months. I’ve been scraping thousands of U.S. data science job postings, analyzing the data, and writing about the findings here.

But at some point, I realized something: the dashboard I built for the analysis was more useful than anything I was writing about it.

So I decided to open source it.

What the dashboard covers

You can explore:

• The top skills companies are actually hiring for, ranked by frequency

• Skills broken down by category (ML/DL, GenAI, Cloud, MLOps, etc.)

• What percentage of roles now require AI skills, broken down by seniority level

• The salary premium for candidates with AI skills

• An interactive explorer where you can browse individual postings with matched skills highlighted

The skill extraction is built on around 230 curated keyword groups, so it’s pretty granular.

Explore the dashboard

Why open source?

I’ve always believed that the best way to help people navigate this market is to show them the data, not just tell them what I think about it.

The code, the data, and the full methodology are all available in this GitHub repo. You can fork it, run your own analysis, or build on top of it.

And by the way, I’m scraping weekly, and I’ll soon be uploading all of the raw data to Kaggle as well. For now, everything is in the repo.

What’s next?

I plan to keep updating this throughout the year and revisit the analysis before the summer. Just like the one I did early this year:

If you find it useful, give the repo a star and share the dashboard with someone who might benefit from it. That’s the best way to say thank you and support this work.

Oh and of course, if you spot anything off or have ideas for what to add, I’d love to hear from you.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope you find this dashboard useful.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

I’ve been hearing this a lot from data scientists lately. People who are genuinely good at what they do, but feel overwhelmed by how fast everything is changing.

Not sure where to focus. Not sure if what they’re learning even matters.

And honestly, it wasn’t that long ago that I felt that way too. Until I started approaching my learning a lot more strategically.

You see, for the past year, I’ve been obsessed with answering one question: What does it really take to future-proof my data science career? Not just in the context of AI, but in terms of how the field is evolving over the next few years.

That shift in focus has genuinely transformed my career, and for the first time ever, I feel like I’m ahead of the curve, not chasing it.

Today, I’m actively championing AI adoption at my company. I’ve been able to amplify my output at work in ways that have led to promotion discussions much sooner than ever before. I’m a stronger candidate in the market despite the competition. I’ve even been invited to give talks on agentic analytics.

But I didn’t get here by learning passively.

I got here by building, by studying the market, and by figuring out what actually matters. I’ve analyzed thousands of job postings to understand what companies really want from data scientists right now, and the picture is much clearer than the noise on social media makes it seem.

This article is my attempt to lay it all out in one place for anyone who needs it.

A practical, no-BS guide to future-proofing your data science career in 2026-2027, covering the skills, the strategy, and the mindset shifts that are actually making a difference right now.

Here’s what we’ll cover:

1. What the job market actually looks like right now (based on thousands of job postings analyzed)

2. The three pillars every data scientist should be investing in

3. Agentic analytics: what it is and why you should (really) care

4. Should you transition to AI engineering? (My honest advice)

5. A practical learning roadmap to help you get started

6. The most effective way to future-proof your career for AI 👈

You’ll definitely want to read this one until the end!

What’s actually happening in the job market

Let’s start with the data, because there’s a lot of noise out there and most of it falls into either “data science is dead” panic or “it’s all just hype” copium.

Neither is accurate.

Earlier this year, I scraped and analyzed 700+ Data Scientist job postings in the US spanning November 2025 through January 2026. I also drew on findings from a Harvard study that tracked nearly 285,000 companies and a report by Lightcast on generative AI hiring trends. Here’s what the picture actually looks like:

The fundamentals haven’t changed, but AI is now expected

Python, SQL, and machine learning still dominate job postings. No surprise there. But AI is now referenced in almost 60% of data science postings, and LLMs have entered the top 10 most requested skills compared to the previous year.

AI isn’t a niche specialization for data scientists anymore. It’s becoming part of the baseline.

Senior roles are growing. Entry-level is tightening.

The Harvard study showed something important: when companies adopt generative AI, junior hiring slows down while senior roles keep growing. Companies aren’t mass-firing juniors, but they’re hiring fewer of them. The routine work that used to justify those headcounts (cleaning data, writing basic code, drafting reports) is now handled more efficiently with AI.

💡 The door into many companies is narrower, not closed. And for those already inside, opportunities haven’t disappeared. In fact, internal promotions for juniors actually went up in companies adopting AI.

AI-heavy roles skew mid-to-senior

When I looked at which seniority levels are being asked for AI skills, the pattern was clear: 73% of AI-focused postings target mid (43%) and senior (30%) data scientists. Entry-level accounted for less than 6%.

This reinforces what I’ve been saying for a while now: companies mainly expect experienced practitioners to be the ones who translate AI capabilities into real systems and business outcomes.

There’s an emerging salary premium

Roles that explicitly mention AI tend to show higher median salaries across most seniority levels. The signal is still early (salary data was only available for about a third of postings), but it’s directionally clear: AI skills are starting to pay more.

💡 If you want the full breakdown, including which specific AI skills are most in demand, I published the complete job market report here.

The three pillars of a future-proof data science career

Now that we’ve seen what the market is asking for, let’s talk about how to respond.

What I’m about to share are the three areas I believe every data scientist should be investing in right now, regardless of seniority (although how you prioritize each does depend largely on your goals and your seniority).

But I want to be clear about why these three specifically.

The goal isn’t just to “keep up with AI.” The goal is to make yourself full-stack enough to navigate the market in almost any direction. Whether you want to stay in a data science role, move into AI engineering, step into leadership, or even build something on your own, these three pillars give you the foundation to choose.

And that’s the real future-proofing, in my opinion. Not clinging to a job title, but building the kind of skills that give you options no matter how the field evolves.

Pillar 1: AI-Powered Data Scientist

This is about making AI a core part of how you work, not just a side tool you occasionally prompt.

Right now, most data scientists are using AI for ad-hoc tasks: fixing code, generating documentation, and summarizing results. And that’s fine as a starting point. But the gap between that and what the market is starting to expect is widening quietly. If that’s where your AI usage stops, you’re building on a false floor.

The next level has two sides to it.

1. The first is using AI to amplify your existing work. Automating data cleaning and early-stage EDA. Speeding up reporting. Getting through the repetitive parts of your workflow faster so you can focus on higher-leverage thinking. This alone can dramatically increase your output and make you more effective in your current role.

2. The second is building AI systems that create new value. Designing semantic layers that let AI reason over your company’s data. Enabling conversational analytics so stakeholders can query data in natural language instead of waiting on you. Building custom knowledge sources and RAG systems that give AI accurate, context-aware access to real datasets.

That combination, amplifying your work and building systems others depend on, is what separates someone who uses AI from someone who is indispensable because of it. It’s also what has made the biggest difference in my own career recently.

💡 About 1 in 3 AI-related job postings (31%) now require hands-on expertise across multiple specific AI skills: LLMs, RAG, prompt engineering, vector databases, and more. This isn’t conceptual. Companies want people who can build.

Pillar 2: ML & MLOps

Despite not getting as much attention as AI, this one is more relevant than ever.

The data science role is becoming more full-stack. Job descriptions are increasingly expecting data scientists to build ML/AI systems end-to-end, from development through deployment and monitoring. Companies don’t just need insights anymore. They need robust, scalable solutions that deliver value beyond the notebook.

If you’re not already investing time in things like:

• Model deployment and monitoring

• System design for ML pipelines

• Tools like Vertex AI, MLflow, or similar platforms

• Python packaging (Poetry, UV) and data validation (Pydantic)

...you should start. These aren’t “nice to have” skills anymore. They’re showing up in job descriptions for standard Data Scientist roles, not just ML Engineer positions.

This is also the pillar that opens the door to AI Engineering if that’s a path that interests you (which we’ll discuss in later sections of this guide). The overlap between a strong data scientist with MLOps skills and an AI Engineer is bigger than most people realize. I’ve been leaning back into this area myself, and it’s been one of the most valuable investments I’ve made.

Pillar 3: Human-First Strategist

Here’s the mistake a lot of people make: they think AI skills alone are enough.

They’re not.

As AI levels the playing field technically (everyone gets access to the same tools, the same models, the same capabilities), your human side becomes the differentiator. The ability to communicate clearly, tell a compelling story with data, and influence decisions is what will set you apart when everyone around you can prompt their way to a decent analysis.

The skills that make you truly resilient in this market are the ones AI cannot replace:

• Business acumen: Connecting your work to outcomes that matter, whether that’s revenue, retention, cost savings, or growth

• Strategic communication: Knowing how to distill complex ideas into something that drives action, not just understanding

• Stakeholder influence: Building the trust and alignment needed for your work to actually shape decisions

• Domain expertise: Understanding the industry and context behind the problems you’re solving, something no model can learn from a prompt

These are about judgment and influence, not just execution. And no matter how good AI tools get, they can’t replicate that.

The data scientists who will stand out in the next few years won’t be the ones with the most technical skills. They’ll be the ones who can pair those skills with the judgment to know what matters, the communication to make it land, and the influence to turn insights into action.

💡 If you want to stay valuable, combine technical proficiency with these human skills. That mix is what makes you more than someone who can query data. It makes you someone who can drive outcomes. I wrote a full article on the traits that accelerated my career growth if you want to dig deeper.

Agentic Analytics: The Shift You Can’t Ignore

If you haven’t heard this term yet, you will. A lot.

Agentic analytics represents a fundamental shift in how data work gets done. Instead of data professionals manually querying databases, building dashboards, and writing reports, AI agents step in to automate and augment large parts of that workflow, using natural language, business context, and structured reasoning.

And the backbone of this shift? The semantic layer and ontologies.

What’s a semantic layer (and why should you care)?

Think of a semantic layer as a shared definition of your company’s data (metrics, business logic, relationships) that sits between raw data and anything that consumes it (dashboards, notebooks, AI agents, stakeholders).

The concept has actually been around since the 1990s. Looker modernized it with LookML, but what’s different now is that semantic layers are no longer just powering dashboards; they’re becoming the interface through which AI agents reason about data.

💡 In September 2025, major companies like dbt Labs, Snowflake, Google, Cube, and others came together to announce the Open Semantics Initiative (OSI), a shared standard for how systems define and communicate business context. This is a big deal.

What does this mean for you?

Here’s my take: by the end of 2026, the idea of a semantic layer will start moving from cutting-edge to expected. We’ll see more “conversational analytics” capabilities across platforms, and data scientists who understand how to build and shape these semantic layers will have a significant edge.

I’ve experienced this firsthand. I recently deployed a talk-to-your-data Slackbot at my company, and one of the keys to its success was defining the semantic layer properly. You don’t need to be a data engineer to start building one. But you do need to understand why it matters.

💡 Agentic analytics, semantic layers, and ontologies are already showing up in job postings. In small numbers now, but my bet is that this will only continue to increase. Data scientists who are part of this design process early will be the ones shaping the next generation of analytics, not just reacting to it. I go into much more detail in my article on Semantic Layers and the Future of Agentic Analytics.

Should you transition to AI Engineering?

This is one of the most common questions I see data scientists wrestling with right now. And it’s a fair one, because the hype around AI Engineering roles is real. The salaries are attractive, the work sounds exciting, and the job postings are everywhere.

But before you start rewriting your LinkedIn headline, here’s what you need to know.

First, understand what AI Engineering actually is

Strip away the hype, and AI Engineering looks a lot like software engineering applied to AI systems. It’s about designing, building, and operating intelligent applications reliably in production. Less emphasis on exploratory analysis and stakeholder storytelling. More emphasis on architecture, state management, evaluation frameworks, and deployment.

It’s a spectrum, not a binary switch

What a lot of people often overlook is that the data science role is already evolving to include more AI-focused work. As the market demands more experience with AI, many data scientists are now building foundation models for tasks like recommendation, designing RAG pipelines, running AI evaluation frameworks, and even orchestrating agentic workflows.

So this isn’t necessarily about changing your title. It’s about where you sit on the spectrum between analysis-focused and systems-focused work, and how far you want to take it.

It might be right for you if...

You might genuinely thrive in AI Engineering if you:

• Feel more energized by building systems than analyzing outcomes

• Enjoy debugging integrations more than interpreting experimental results

• Get excited about architecture decisions, not just model accuracy

• Were always drawn to the engineering side of data science

But it’s not for everyone

If what gives you satisfaction is shaping decisions rather than shipping infrastructure, then moving fully into engineering could disconnect you from what you actually enjoy.

There is good money in AI Engineering, and the work is exciting. But alignment with your interests is what sustains a career over time. That’s what truly future-proofs you, not chasing the hottest job title.

💡 I wrote a full deep dive on this topic: Should You Actually Transition from Data Science to AI Engineering?. Worth reading if you’re seriously considering this path.

A practical learning roadmap

Before I share specific resources, I want to talk about how to approach learning this stuff, because the mindset matters more than the material.

The framework: build, break, learn, repeat

If you take one thing from this section, let it be this: build something, get stuck, go back to the theory, then build again. That loop is the fastest way to actually internalize these skills. Not watching a 10-hour course end-to-end. Not collecting bookmarks you’ll never open.

You start a project. You hit a wall. You go figure out why. Then you come back and keep going.

This also means there’s nothing wrong with “vibe coding” your way through the early stages. Use Claude Code, use Copilot, let AI help you move fast. But know that it will only get you so far. There’s a point where you need to stop asking “does this work?” and start asking “do I actually understand why?”

That’s when the real learning happens. And tools like NotebookLM or ChatGPT are great for bridging that gap, especially when your time outside of work is limited (which, for most of us, it is).

The foundations matter more than people admit. Knowing what’s actually happening inside the loop, or why an architecture behaves the way it does, changes how you build. It changes the questions you ask. And it’s the difference between someone who can follow a tutorial and someone who can solve problems their team actually has.

Resources I recommend

Here are just a few of the resources that have helped me the most. You don’t need to go through all of them linearly. Pick what’s relevant to where you are right now and come back to the rest when you need it.

📚 Books

• AI Engineering by Chip Huyen. The best resource I’ve found for understanding how to build AI systems in practice.

• LLM Engineer’s Handbook by Paul Iusztin. Great for going deep on LLM-specific workflows.

• Designing Machine Learning Systems by Chip Huyen. Essential for anyone investing in MLOps.

• Storytelling with Data by Cole Nussbaumer. A practical guide to communicating insights clearly and driving decisions.

• How to Win Friends and Influence People by Dale Carnegie. Timeless principles for building relationships and influencing others effectively.

🎥 YouTube Channels

• AI Engineer: Huge variety of technical talks on building AI systems.

• LangChain: Lots of great tutorials on building workflows and agentic systems.

• Andrej Karpathy: Deep, foundational understanding of how LLMs work.

👨‍💻 Other gems

• Introduction to LangGraph (Free course by LangChain)

• System Design Concepts Course and Interview Prep by freeCodeCamp.org

• Andrej Karpathy’s 1hr Intro to Large Language Models

• Building Effective Agents with LangGraph

Where to start

If you’re not sure where to begin, here’s a simple path that works:

1. Watch Andrej Karpathy’s 1hr Intro to Large Language Models: This gives you the foundational understanding of how LLMs actually work, which changes how you approach everything else.

2. Pick up AI Engineering by Chip Huyen: You don’t need to read it cover to cover. Focus on the chapters that are relevant to what you’re trying to build.

3. Build a simple LLM-powered data cleaning script: Call an LLM API, pass it a prompt that includes an overview of your dataset, and have it identify missing values and generate the code to clean it. Simple, useful, and the first step from using AI to building with it.

4. From there, go deeper: Explore agent frameworks like LangGraph, experiment with building more autonomous workflows, and start thinking about how these tools could solve real problems at your company.

Start small. Get it working. Then improve it.

💡 There are plenty of other great resources out there beyond what I’ve listed here. Don’t overcomplicate it. The path above is more than enough to get moving.

🚀 This is the most effective way to future-proof your career for AI

Everything you just read about becoming an AI-powered data scientist, the semantic layers, the agentic workflows, and building systems your team depends on, I had to piece together on my own over the course of a year. Through trial and error, building at work, studying on weekends, and understanding what the market actually rewards.

You could do the same. But it doesn’t have to take a year (or even months)

That’s why I packaged it all into the AI Workflows for Data Science bootcamp, a 6-week structured program that covers the applied AI skills this guide lays out in Pillar 1. It gives you the roadmap, the hands-on projects, and the support system to go from reading about these skills to actually building with them, in a fraction of the time it took me.

Built specifically for data scientists, by a data scientist, focused on the skills the market is demanding right now.

It’s already helped 40+ data professionals make this transition. If you’re serious about staying relevant in the market, this is the most effective path I know:

Learn More

A final note

If you’ve made it this far, you are already ahead of 80% of data professionals out there.

The truth is, no one has this fully figured out, including me. I’m navigating the same uncertainty, making bets on where the field is heading, and adjusting as I learn.

But here’s what I do know after 8+ years in this field, after getting laid off, moving countries, going from analyst to analytics team lead, and now building AI workflows in production:

The people who will thrive aren’t the ones who learn the most tools. They’re the ones who build the right skills, stay curious, and actually ship things.

The market is shifting. The window to get ahead of that shift, instead of playing catch-up, is still open. But believe me, at the rate we are moving, it won’t stay open forever.

So pick a pillar. Start building. And don’t wait until you feel “ready,” because honestly, no one ever does.

You’ve got this. 🙏

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this breakdown helps you get started future-proofing your career.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

The focus there is not on tools, but on frameworks and end-to-end systems.

And that’s where the real signal is.

If you want to understand how far AI could transform how we do data science over the next few years, you need to look at the direction these systems are heading.

So I’ve gathered 6 research papers covering recent advances in autonomous AI agents designed to automate data science, machine learning research, and even broader scientific discovery.

At the end, I also share my take on what this actually means for us today and how we should prepare.

1. DeepAnalyze: Autonomous Data Science via Agentic LLMs

This research paper introduces DeepAnalyze-8B, an agentic model trained to autonomously handle the entire data science pipeline from raw data to professional research reports. It uses a curriculum-based training paradigm to acquire complex problem-solving capabilities similar to those of a human data scientist.

2. AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

This paper studies AI agents that compete in Kaggle competitions by exploring solution spaces using search strategies like Monte Carlo Tree Search and evolutionary methods. The study shows that pairing these search policies with well-designed operators significantly improves performance in real ML engineering tasks.

3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

This paper introduces an autonomous framework that can perform the entire ML research lifecycle: generating ideas, running experiments, analyzing results, and writing full research papers. It even includes an automated LLM-based peer-review system to evaluate the generated work.

4. Jupiter: Enhancing LLM Data Analysis Capabilities via Notebook and Inference-Time Value-Guided Search

This paper introduces NbQA, a large dataset of real Jupyter notebook analysis tasks, and JUPITER, a framework that treats notebook-based data analysis as a sequential decision-making search problem. A value model guides the reasoning process to efficiently solve multi-step data analysis tasks.

5. DS-STAR: A state-of-the-art versatile data science agent

This paper by Google proposes an agentic system designed to handle real-world data science workflows involving heterogeneous data sources such as CSV, JSON, and text. It decomposes analysis into sub-questions and iteratively builds evidence-based reports to minimize hallucinations.

6. Kosmos: An AI Scientist for Autonomous Discovery

This paper presents a scientific discovery agent that coordinates literature search and data analysis through a structured world model. By running long autonomous research loops, it can read thousands of papers and execute large-scale experiments to generate traceable scientific discoveries.

The common denominator

Across these papers, one thing becomes clear: we are moving from AI as a tool to systems that can operate across the entire data science lifecycle. Not just assisting with isolated tasks, but handling the full loop, from defining problems to running analyses and producing research-level outputs.

A big part of this shift comes from how these systems are designed. They don’t rely on a single pass. Instead, they iterate. Through feedback loops, search strategies, and self-reflection, they refine their outputs step by step, getting closer to something that actually resembles how a data scientist would approach a problem.

When you combine this with large-scale training data, like real Jupyter notebooks, and more structured ways of interacting with data and external knowledge, the result is systems that can reproduce fairly complex reasoning.

There are a few patterns that show up consistently across these papers:

• End-to-end ownership: These systems don’t just assist, they handle the full workflow, from ideation to final output.

• Iterative refinement: Instead of one-shot answers, they rely on loops, evaluation, and correction.

• Decomposition of problems: Open-ended questions are broken down into smaller, verifiable steps grounded in data, code, or literature.

• Built-in evaluation: With automated reviewing and benchmarks, they can critique and improve their own outputs.

Another important shift is reliability. By grounding their reasoning in executable steps and verifiable sources, these systems start to reduce one of the biggest limitations we’ve seen so far: hallucinations in open-ended tasks.

Put together, this starts to look less like a model you prompt, and more like a system that can iterate on its own work, explore a space of ideas, and converge toward something useful.

My honest take

When I shared these papers on my LinkedIn, I got a comment that stood out.

Because, regardless of how far these frameworks go in automating the analysis process, the question remains the same:

Can they actually operate under real-world constraints, in companies where more than accuracy matters, and where business context is far more nuanced than what can fit into a few documentation pages?

For the foreseeable future, I still see a strong need for a human-in-the-loop, regardless of how capable these models become or how large their context windows get (and by the way, Claude just made their 1M context window widely available for their latest models).

Still, the limiting factor isn’t intelligence, it’s context.

And more specifically, how that context is structured, injected, and continuously updated. In practice, a large part of what drives good analysis is not explicitly written down. It lives in things like:

• Shifting priorities across teams

• Implicit assumptions behind metrics and decisions

• Trade-offs that only emerge through experience and stakeholder context

This is the layer that doesn’t neatly fit into prompts or documentation.

That said, I do think there are specific environments where these agents will start defining and evaluating their own hypotheses. In well-scoped problems, with reliable data and clear feedback signals, they have a clear advantage. They can explore a much larger hypothesis space and validate ideas at a speed that is simply not possible for a human.

Where this becomes powerful is in narrowing the gap between exploration and validation, not by removing the human, but by making it possible to test more ideas, faster, without losing control over what actually matters.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this breakdown gives you a glimpse into the future of data science.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

📌 By the way, enrollment for the April cohort of my AI workflows Bootcamp is now open. This 6-week program is designed to help data scientists develop the judgment, systems thinking, and applied AI skills that amplify their impact and set them apart in the market.

There’s a lot of talk right now about how AI is transforming the role of data scientists.

And well, depending on who you listen to, our jobs are either about to disappear or about to turn into something completely different.

So I thought it might be useful to do something simple, show you what my job actually looks like today, working at a scale-up that is adopting AI very fast.

And I can tell you right now that while AI is definitely changing the way I work, the reality of the job probably looks different from what most people imagine.

Let’s break it down.

Yes, I Still do Data Science

If you shadowed me for a week, most of what you’d see would still look like a fairly traditional data science role in tech.

I spend a large portion of my time working on projects that support product and business decisions.

Typical questions I work on include things like:

• Retention and engagement analysis

• Understanding user behavior

• Evaluating product changes through A/B testing

• Marketing and growth analytics

At one point, I built a Markov model to better understand user behavior across the product. Not exactly the type of model that gets people excited on LinkedIn these days, but still extremely useful.

And by the way, I still spend more time writing SQL than Python.

So even in 2026, a lot of my job is still about applying the fundamentals well.

My Week Is Mostly Deep Work (Thankfully)

One thing that might surprise people about my current role is the number of meetings I have.

In my case, it’s actually quite healthy. My calendar usually includes only a few recurring meetings:

• Standups with the data team

• Sprint planning sessions

• Occasional sessions based on projects and syncs with stakeholders

The rest of the time is mostly dedicated to deep work on projects that span weeks or sometimes an entire quarter.

That said, this hasn’t always been the case. Earlier in my career, I had roles where I was much more embedded with stakeholders and spent significantly more time in meetings.

What I’m experiencing now has a lot to do with my seniority level and the company culture at Nextory. There’s a strong understanding that if data scientists are buried in meetings all day, it becomes very difficult to actually produce meaningful work.

Right now, this structure allows me to focus much more on building, analyzing, and pushing projects forward.

AI Is Now Embedded in My Workflow

Now, the part that has changed significantly is how I work day-to-day.

AI is embedded into almost everything I do.

At Nextory, we are actually encouraged to experiment with AI tools and share how we’re using them to improve our work. In fact, I’m one of the stewards in the company helping promote AI adoption internally.

So how do I actually use AI?

Mostly to amplify my productivity.

For example, I regularly use AI to:

• Write SQL queries faster

• Automate parts of data cleaning and EDA

• Generate complex Python code for analysis or modeling

• Brainstorm analytical approaches

• Summarize analysis results

• Write documentation

• Navigate our data warehouse and help improve data models

I also rely heavily on tools like Cursor for coding, Gemini inside the GCP ecosystem, and ChatGPT for brainstorming ideas and structuring analysis, and the same is true for my coworkers.

At this point, AI feels less like a separate tool and more like an extra layer in the workflow.

My role is a clear example of the impact of AI in our field and exemplifies the trend we are already seeing in the job market. I recently did a full analysis on this in case you are interested:

AI Hasn’t Replaced the (Real) Hard Parts

When people talk about AI changing data science, the conversation often focuses on automation: writing SQL faster, generating Python code, and building dashboards with fewer manual steps.

And yes, those parts are changing.

But what AI hasn’t replaced is the core responsibility of the job.

Things like:

• Stakeholder communication

• Defining the right questions

• Building trust with teams

• Driving adoption of insights

AI can help me brainstorm, it can help me move faster, but it still depends heavily on the context I provide.

Most of the time, I’m the one guiding the process and setting the constraints. In other words, AI speeds up the work, but the judgment still sits with me.

We’re Also Building Complex AI Systems

Beyond using AI to improve my own workflow, we’re also starting to build AI-powered systems inside the company.

One example is a Talk-to-Your-Data Slack bot that I built for stakeholders. The idea was simple: allow non-technical stakeholders to ask questions about our data using natural language and get insights faster without needing to write SQL.

It was our first step toward something closer to what people are now calling agentic analytics.

At the same time, other teams in the company are building much more sophisticated AI systems that are user-facing. For example, Nextory recently launched an AI Librarian designed to help users discover books through conversational interactions.

Projects like that involve data scientists, software engineers, and ML engineers working together to build production-grade AI systems that directly impact the product experience.

What’s interesting is that this is creating a wider spectrum of roles.

Some data scientists (like myself) remain closer to the business layer, focusing on analysis, experimentation, and decision-making. Others are moving closer to the engineering side, helping design and build the AI systems that power new product capabilities.

If you want to know more about this transition towards an engineering-focused role, then you want to read this article:

Final Thoughts

If someone shadowed me for a week, the biggest surprise probably wouldn’t be that I use AI.

It would be how much work I’m able to accomplish because of it.

The fundamentals of the job are still there: understanding problems, analyzing data, and helping teams make better decisions.

But AI is amplifying the work. It allows me to move faster, automate parts of my workflow, and explore complex ideas in days rather than weeks or months.

And from a career perspective, it’s also clear that these skills are becoming increasingly valuable. Being able to combine data science fundamentals with AI-driven workflows gives me a level of leverage that simply didn’t exist a few years ago.

Whatever happens in this role, I know those skills will transfer well. The tools are evolving quickly, but the core of the work remains the same.

And for me, that combination is exactly what makes this moment in the field so interesting.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this breakdown gives you a more grounded perspective on how AI is disrupting our field.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

“Is it time to transition into AI Engineering?” “Is it too late?”

If you spend even a few minutes on LinkedIn, it can feel like the center of gravity has shifted. Suddenly, everyone is building agents, deploying RAG pipelines, experimenting with orchestration frameworks, and rebranding themselves as AI Engineers.

But before you change your title, you need to answer a deeper question:

Are you genuinely drawn to that work…or are you reacting to the fear of becoming irrelevant?

Let’s slow it down and look at it properly.

What AI Engineers Actually Do

Strip away the hype, and AI Engineering looks a lot like software engineering applied to AI systems. It’s about designing, building, and operating intelligent applications reliably in production.

In practice, this often means:

• Designing LLM-powered applications

• Implementing retrieval-augmented generation (RAG) pipelines

• Orchestrating multi-step workflows or agents

• Connecting models to APIs, databases, and internal tools

• Monitoring performance, latency, cost, and reliability

The focus is far from discovering insights in a dataset and more on constructing systems that consistently deliver value at scale.

You’re thinking about architecture, state management, evaluation frameworks, logging, versioning, and deployment environments. You’re asking questions like:

• How do we make this robust?

• How do we reduce hallucinations?

• How do we measure quality over time?

Compared to traditional Data Science roles, there is typically less emphasis on exploratory analysis, hypothesis-driven experimentation, and stakeholder-facing storytelling.

That doesn’t make one role superior to the other, but it does mean they reward different strengths.

Data Science Is Already Evolving

I often talk to people seriously considering this transition, and what they often overlook is that Data Science hasn’t stayed static.

Modern data scientists are increasingly expected to:

• Write production-ready code

• Own pipelines end-to-end

• Collaborate closely with engineering

• Think about deployment and monitoring

• Use AI tools to accelerate analysis

The “pure modeling” specialist is not disappearing (anytime soon at least) but is becoming rarer outside very large organizations. In many teams (like the one I’m currently in at Nextory), the line between Data Scientist and AI Engineer is already blurred. You may not carry the AI Engineer title, but you’re doing parts of the job.

So this transition isn’t a binary jump. It’s a movement along a spectrum.

If you want to learn more about what this looks like in practice, check out this job market report:

Why You Might Want to Transition

But okay, let’s figure out if this is actually the right move for you, because you might genuinely thrive in AI Engineering if you feel more energized by building systems than analyzing outcomes.

If your curiosity is triggered by questions like:

• How should this architecture be designed?

• What’s the cleanest way to structure this workflow?

• How do we evaluate this agent automatically?

If you enjoy debugging integrations more than interpreting experimental results, that’s a signal.

Some data scientists were always drawn to building intelligent systems. For years, that meant moving toward ML Engineering or MLOps. Now, AI Engineering is another branch of that same instinct.

If you’ve consistently been more excited by building and operationalizing systems than by running experiments or presenting insights, this isn’t hype; it’s alignment.

Why You Might Not Want To…

Many data scientists chose this path because they thrive in ambiguity and enjoy framing messy business problems, designing experiments, and translating data into decisions.

There is a creative element in analysis that often goes unnoticed: defining the right metric, interpreting conflicting signals, identifying behavioral patterns, and influencing product strategy.

If what gives you satisfaction is shaping decisions rather than shipping infrastructure, then moving fully into engineering could slowly disconnect you from what you actually enjoy.

In reality, most data scientists won’t fully “switch” roles. They’ll become more AI-native instead. That means understanding how LLM systems work, being able to prototype AI workflows, knowing how to evaluate outputs, and collaborating effectively with engineers who own the deeper infrastructure.

The strongest profiles will combine business context, analytical rigor, engineering awareness, and AI literacy. The future is less about changing your title and more about expanding your toolkit.

Why I’m Not Transitioning

Personally, I’m not moving into a full AI Engineering role, at least not right now.

That’s not because I’m ignoring where the field is going. I’m already building AI systems to automate parts of my work and have built AI-driven products on the side, and teaching practical AI workflows has become a core part of what I do.

The reason is simpler: I genuinely enjoy staying close to the business layer. Shaping metrics, designing experiments, and connecting analysis to strategy is what feels most meaningful to me.

If I moved too far into full-time infrastructure and architecture, I’d lose the proximity to decision-making that makes the work fulfilling.

Final Thoughts

The real risk isn’t choosing the wrong title. The real risk is drifting into a role that doesn’t align with your strengths simply because the market shifted.

There is good money in AI Engineering, and working with emerging technology can be genuinely exciting. But alignment with your interests and the kind of work that actually energizes you is what sustains a career over time.

That’s what truly future-proofs you.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this guide helps you decide what is the best path for you.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

Many of them I’ve run into while working on projects, experimenting on my own, or simply paying attention to what the community is building. Some stood out because they help with very concrete parts of the job, like exploration, feature work, forecasting, and working with messy inputs.

Here are my absolute favorites so far.

1. AI Sheets

AI Sheets lets you enrich, label, and transform tabular data using LLMs directly inside a spreadsheet-like interface.

In practice, this is useful for things like generating features, classifying text columns, or adding weak labels to datasets before training a model.

2. Data Formulator

Data Formulator turns natural-language descriptions into concrete data transformations and visualizations.

It’s especially useful during early EDA, when you’re iterating on how to slice the data and don’t want to hand-write transformations you might throw away five minutes later.

3. Jupyter AI

Jupyter AI brings LLM assistance directly into Jupyter notebooks, tightly coupled with your code and variables.

This works well for refactoring analysis code, explaining unfamiliar notebooks, or quickly prototyping modeling steps without breaking the notebook workflow.

If you want a more in-depth view of this tool, check out this article.

4. PandasAI

PandasAI lets you ask questions about a DataFrame in natural language and executes the corresponding pandas operations.

It’s not a replacement for pandas, but it’s very effective for speeding up EDA and sanity checks when you already know what you want to inspect.

I wrote a two-part series on how to get the most out of PandasAI. Here is the article.

5. ChartDB

ChartDB automatically generates visual representations of database schemas and table relationships.

This is particularly helpful when onboarding onto a new data warehouse and trying to understand how raw tables connect before writing models or features.

6. MCP Toolbox for Databases

MCP Toolbox exposes databases as structured, permissioned tools that LLM agents can query safely.

This is a foundational piece if you’re building agentic analytics systems that need database access without giving an LLM free-form SQL over production data.

If you are ready to start building MCP servers, read this article.

7. TimeGPT

TimeGPT is a pretrained foundation model for time-series forecasting that works out of the box via an API.

It’s a strong baseline when you want fast, reasonable forecasts without spending days on feature engineering and model tuning.

If you want to learn more about foundation models, check out this article.

Bonus: MarkItDown

MarkItDown converts PDFs, Word files, and presentations into clean, structured Markdown.

For data scientists, this is especially useful when preparing documents for retrieval pipelines, evaluation datasets, or any RAG-style setup.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this guide helps you get the most out of your AI tools.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

It’s what separates good data scientists from great ones.

It’s what every employer is looking for in their next hire.

And it’s what defines your value as a data scientist.

But creating impact takes more than just technical skills. It’s not enough to build great models; you need to make them accessible and usable.

That’s why deployment is so important.

A model sitting in a Jupyter notebook isn’t doing anything. To create real value, you need to deploy it.

So today, I want to show you how to do just that using Streamlit

But going beyond the basic Streamlit Cloud.

This is the same method I’ve used to coach many of my mentees in the past, helping them build projects that impress hiring managers and get hired.

Here is what we’ll cover:

1. Why Streamlit Cloud isn’t enough

2. How to Dockerize your Streamlit app (practical guide)

3. Deploying a Dockerized Streamlit app

1. Why Streamlit Cloud Isn’t Enough

Nowadays, using Streamlit to bring your portfolio projects to life is a must. It’s a great way to show your ability to build projects end-to-end.

Well, sort of.

Most projects default to deploying using Streamlit Cloud. Just a few clicks, and it’s live for anyone to use. Easy! Great for quick prototypes.

But easy comes at a cost.

The technical skills don’t always transfer beyond Streamlit’s ecosystem. And the idea here is to show we can go beyond simple prototypes.

This is where Docker comes in.

With Docker, you’re no longer just deploying an app; you’re packaging it in a way that works anywhere.

And guess what? It’s the industry standard for containerizing in software development, including data science and machine learning workflows.

If you are new to Docker, I recommend you first read this article to familiarise yourself with the key concepts:

Otherwise, here is a quick look at our strategy:

Let’s break it down.

2. How to Dockerize Your Streamlit App (Practical Guide)

Deploying with Docker means you’re not just launching an app, you’re creating something that works anywhere.

But to do that, you need a Dockerfile, which works as a simple script that tells Docker how to package your app:

• What dependencies to install

• What files to include

• How to run it.

Let’s see what that looks like

Minimal Dockerfile template (ready-to-use)

Here’s a simple Dockerfile that works for Streamlit.

That’s it. Minimal, efficient, and does the job.

But of course, this is just a starting point. You can always tweak it to meet your specific needs (e.g., you can use a later version of Python).

You can download the exact template here.

Running your Docker container locally

Now, let’s test it before deployment.

Step 1: Build the Docker image (only needs to be done once):

docker build -t my-streamlit-app .

Step 2: Run the container (this starts your app inside Docker):

docker run -p 8501:8501 my-streamlit-app

Step 3: Visit http://localhost:8501 and boom, your app is running inside Docker.

At this point, you’ve Dockerized your Streamlit app. Now, let’s get it live.

3. Deploying a Dockerized Streamlit App on Render

Now that your app runs locally, it’s time to deploy it properly.

I recommend using Render.

Why Render?

• Free tier (so you don’t burn cash while testing).

• Supports Docker natively (which means no weird config issues).

• Simple setup (no Kubernetes, no headaches).

Let’s deploy.

Simple steps to deploy a Dockerized app

1. Push your project (with the Dockerfile) to GitHub (if it’s not there already).

2. Sign up on Render and create a new Web Service.

3. Connect your GitHub repo and select the branch with your Dockerfile.

4. Under “Language,” choose “Docker.”

5. Click “Deploy.”

💡 Render will automatically build your Docker image from your Dockerfile, you do not need to push an image to a container registry.

Once it’s live, you’ll get a public URL to share your deployed project.

And just like that, you’ve taken your Streamlit project beyond just a “quick” prototype.

But…what if you already have a Docker image?

Alternatively, you can also use Render to deploy a prebuilt Docker image from a registry such as Docker Hub.

💡 This is useful when you want faster deployments or when the image is already built as part of a CI pipeline. In production teams, this is often the standard setup: build once, deploy many times.

Here is the full documentation for this approach.

Just know that both approaches are valid. The difference is where the image is built and how that build step is managed.

Ultimately, the real goal is to use Docker to make your application portable and reproducible, turning a local project into something that can be reliably deployed in a real environment.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope you found these tips insightful.

See you next week!

- Andres

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

The main takeaways from those two studies were as follows:

1. The need for data scientists is not disappearing; in fact, there is an increasing demand for data talent that knows AI

2. Senior roles are benefiting the most from AI adoption

You can read more about how those conclusions were drawn here.

Those findings were directionally clear, but they were also high-level.

They told us what was happening, not necessarily how it was showing up in actual hiring. And given how fast AI tools and expectations have evolved over the past year, I didn’t feel comfortable assuming the story had stayed the same.

That’s why I wanted to look closer.

So I scraped and analyzed 700+ Data Scientist job postings in the US, spanning November through January 2026. This report is not meant to be definitive, but it provides a strong early signal into how demand for AI skills is actually shaping up as we enter the year.

I will cover:

• What’s changed since the last analysis?

• Which AI skills are actually being asked for in data scientist roles today?

• Who is feeling the impact of AI demand the most?

📌 And at the end, I’ll share some strategies for how data scientists can adapt their skill sets and stay competitive.

Let’s get to it!

The core skill stack for data scientists hasn’t changed, but AI adoption has accelerated

Looking at the most mentioned skills across all data science job postings, the fundamentals remain stable: Python, machine learning, and SQL still dominate.

What stands out is that AI has increased in demand, referenced in more than half of all postings. And most notably, LLMs have entered the top 10 when compared to the previous year. This suggests that AI is no longer framed as a niche specialization, but as an expected extension of the standard data science toolkit.

That said, not every mention of the keyword “AI” implies deep technical ownership, many roles reference AI in a broader sense, such as collaborating on or supporting AI-related work.

Within AI-focused data science roles, LLMs clearly lead skill demand.

Zooming in on jobs that explicitly mention AI, LLMs emerge as the most requested AI skill, followed closely by GenAI and NLP.

Beyond these top three, demand shifts toward more applied capabilities such as RAG, AI agents, prompt engineering, and orchestration, indicating that companies are increasingly looking for data scientists who can work with AI systems in practice, not just conceptually.

AI-heavy roles skew toward mid-to-senior Data Scientists

When we look at where these AI requirements appear across seniority levels, the demand is far from evenly distributed.

The majority of AI-focused postings target mid and senior data scientists, reinforcing the idea that companies expect experienced practitioners to translate AI capabilities into real systems and business outcomes.

By the way, this aligns closely with the earlier Harvard findings, where the benefits of AI adoption are most concentrated in more senior roles

Some data scientist roles want AI literacy, others want hands-on builders

The distribution of AI skill requirements shows a split in how companies think about AI capability. Many roles still ask for a small number of broadly defined AI skills, but a significant portion now require experience across multiple concrete areas, such as LLMs, RAG, prompt engineering, and vector databases.

Here we can see a snippet of what this looks like across different job postings:

This points to growing demand for data scientists who can actually build and deploy AI-driven systems, not just understand them conceptually.

AI skills are mostly embedded in standard Data Scientist roles

Even when AI skills are explicitly required, the majority of postings still use standard titles like “Data Scientist” (which only specify seniority, location, or traditional domains), with AI referenced in the skill requirements rather than the role name.

Only a smaller share of roles explicitly call out AI, GenAI, or LLMs in the title, indicating that, at least in the current data, AI expectations are primarily expressed through requirements, not naming conventions.

Preliminary findings: AI-focused data science roles are beginning to show a salary lift

Across most seniority levels, data science roles that explicitly mention AI tend to show higher median salaries than comparable roles that do not. This suggests an emerging AI-related salary premium within data science, but the signal is still early.

Salary data is only available for roughly a third of postings, and some seniority bands have limited samples, so this should be interpreted as directional rather than conclusive.

Summary

Here is a recap of the key findings:

• Are AI skills in demand for data scientists? Yes, about 60% of postings expect AI capability, either broadly or through specific skills like LLMs, RAG, and AI agents.

• Which AI skills should I focus on? Experience with LLMs was the #1 AI skill requirement, appearing in ~20% of all job postings.

  • About 1 in 3 AI-related postings (31%) require hands-on expertise across multiple specific AI skills: LLMs, RAG, Prompt Engineering, vector databases, and more. This signals growing demand for practitioners who can build and deploy AI systems, not just understand AI conceptually.

• Which roles are the most impacted? AI demand concentrates at mid-to-senior levels: 73% of postings mentioning AI skills target mid (43%) and senior (30%) roles, while entry-level positions account for less than 6%.

• Are job titles evolving? Currently, 73% of Data Scientist postings with AI requirements use standard titles like “Data Scientist” (and only specify seniority, location, or traditional domains) while 27% explicitly call out AI/GenAI/LLM in the job title, suggesting AI skills are increasingly expected within traditional roles.

• Any other trends I should keep an eye on? Agentic Analytics, Semantic Layers, and ontologies are already being mentioned, although in just a handful of jobs. My bet is that this will only continue to increase as the year progresses.

One thing is clear: AI is becoming a core part of the data scientist role, and it is already influencing expectations, scope, and career progression.

Here is my proven roadmap to build these skills in 2026

Everything I’ve shared so far reflects clear patterns already taking shape across the field and what they imply for data scientists going forward.

Over the past year, one way I’ve personally responded to these shifts is by building and refining a bootcamp focused on agentic analytics, AI workflows, and system-level thinking. It’s designed for data scientists who want to move beyond isolated analyses and start building end-to-end systems that actually scale their impact.

So far, we have helped over 40 data professionals future proof their careers. This is what some of the alumni had to say:

Enrollment for the April 2026 cohort is now open, but seats are limited:

Enroll today

Enrollment closes in only 6 days. Keep in mind that if you miss this cohort, the next one won’t be available until later in the year.

Regarding the analysis approach

My plan is to continue scraping job postings throughout the year and revisit this analysis again before the summer.

In the meantime, I’m going to clean up the Streamlit app I used for the analysis, deploy it, and make both the implementation and the scraped data open source so others can build on it or run their own analyses.

Thank you for reading! I hope you found this article insightful. And don’t forget to look into the “AI Workflows“ bootcamp, enrollment closes in just a few days.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

We saw AI truly disrupt the job market and the field itself, raising fundamental questions about the future of data science. At the same time, much of what we consumed online and in the news was wrapped in a thick layer of hype and misinformation.

No, data science is not “dying“. And no, AGI does not seem anywhere close, yet. But even though not every promise actually materialised, we did see a set of clear patterns emerge that are simply hard to ignore.

I know this, because I spent the better part of the year:

• Studying the data & AI job market

• Building AI workflows to automate and optimise my own work and future-proof my career

• Attending conferences and having meaningful conversations across the industry

• Running two data science and AI-focused newsletters that forced me to keep up with the news

So instead of making wild predictions, I want to share my bets for 2026, grounded in what we are already seeing today.

And at the end, I’ll share practical advice on what data scientists should focus on to accelerate their careers.

Bet #1: Agentic analytics will take center stage over traditional analytics

2026 will undoubtedly be the year when we start seeing these new types of data science workflows become normalized, where data scientists work alongside AI agents to automate and augment their work.

This goes beyond using tools like ChatGPT or Cursor to write code faster. Instead, we are talking about AI-powered workflows that can:

• Explore data proactively

• Detect patterns or anomalies

• Run follow-up analyses

• Make decisions with minimal human intervention

This is what the industry is calling Agentic Analytics.

To be clear, when I say agentic analytics will take center stage, I don’t mean that traditional analytics disappears. I mean that hand-written, step-by-step analysis stops being the default starting point, and instead becomes something we step into when judgment, nuance, or validation is required.

As a result, data scientists will start acting more as system designers and architects, letting these systems do much of the heavy lifting and getting involved primarily in the parts that require judgment and context, as well as presenting work in a way that actually drives decisions.

This will not happen all at once, though…

We will continue to see a selective group of data scientists lead the way and truly become 10x at what they do. This will impact both performance and the amount of value companies will expect a single data scientist to provide.

Towards the end of 2026, as we prepare to step into 2027, we will see a large wave of data scientists feeling the urgency to catch up. Improving their ability to think more like system designers, build these systems themselves, and adapt to the new tools already establishing themselves in the market.

Bet #2: We will witness the commoditization of the semantic layer

The modern data stack is no longer enough.

Companies will increasingly feel the need to move toward a combined data and AI stack.

As a result, having a semantic layer will become a standard. Not as a nice-to-have, but as a prerequisite for keeping up with the next wave of agentic analytics.

This shift will highlight the value data engineers bring to the organization. But building and maintaining a semantic layer will not be a purely engineering problem. It will require the involvement and judgment of data scientists, especially when deciding how data should be interpreted, exposed, and used downstream.

For those organizations that have already adopted a semantic layer to support their Business Intelligence, we will see them optimize it with AI systems in mind. In particular, systems that allow non-technical stakeholders to “talk to their data” and request insights using natural language.

This will give way to a new definition of self-service analytics. One where the semantic layer becomes the shared foundation not just for dashboards, but for AI-driven decision-making across the company.

Bet #3: Foundation models will become central for tackling core data science problems more effectively

Instead of building a new model for every task, more teams will start reaching for foundation models as the starting point.

The reason is simple: they unify solutions. You move away from a fragmented landscape of narrowly trained models and toward shared representations that can be adapted across multiple use cases.

We are already seeing impressive progress in foundation models for anomaly detection and time-series forecasting. And while we will continue to see improvements there, I expect a lot of effort to go into foundation models for recommendation systems and ranking, along with other core areas where companies need models that generalize well and can be reused across products.

We have already seen signs of this direction from Netflix, where the industry is moving away from building isolated recommenders and toward more unified approaches.

What this means in practice is that the “modeling work” will change.

More data scientists will spend time training, adapting, and evaluating foundation models, and less time building task-specific models from scratch. The competitive advantage shifts toward understanding how to apply these foundations to real product problems, and how to ground them in your data, constraints, and success metrics.

Bet #4: The scope of data scientist roles will continue to widen

Over the past few years, we have seen the rise of more specialised roles: MLOps, AI Engineer, and similar titles are becoming more common. Data science, however, is not moving in the same direction.

Rather than becoming more specialised, the role is becoming more full-stack.

The reason is that data science sits at the intersection of business, engineering, and decision-making. That combination is simply hard to replace.

And now that AI has introduced new tools and techniques into the picture, we will see more data science roles pop up that require these types of responsibilities:

• Build ML/AI systems end-to-end.

• Build RAG systems to handle unstructured data

• Train and work with foundation models

• Handle guardrails and AI evaluations

By the way, I’ve done a deep analysis of the job market to understand what new AI tools and techniques are emerging for data science, and I will be sharing my full analysis next week, stay tuned.

And as the AI tools advance to facilitate the creation of these solutions, data scientists will evolve to think more like system designers. Designing, orchestrating, and maintaining their own workflows rather than working on isolated pieces.

Those who remain adaptable will thrive, while those who try to hold onto a narrowly defined version of the role will find it harder to keep up.

To clarify, I don’t believe specialised DS/Analytics roles will disappear. Product analytics, recommender systems, and other focused areas will continue to be in demand. But general data science roles will widen in scope, both by expectation and as a natural evolution for those who want to strengthen their skill set and remain future-proof

Bet #5: AI will turn non-technical stakeholders into entry-level data analysts

This shift is already underway.

Many non-technical stakeholders already have strong analytical instincts. What has been missing is not curiosity or problem framing, but the ability to handle the technical parts of the work. AI tools are increasingly filling that gap by taking care of tasks like querying data, writing code, and running basic analyses.

We have already seen this in product teams, where stakeholders are now able to build their own demos and proof of concepts. But it is not limited to product; we are starting to see the same pattern with data work. Stakeholders can upload CSVs or other structured data and have tools like ChatGPT or Claude analyze it for them, help them write basic SQL, or generate small scripts to answer concrete questions.

As a result, they rely less on specialized roles like data analysts for simple, exploratory work.

This trend is reinforced by two parallel shifts:

• Everyday tools are becoming analytical tools: Excel, Google Sheets, and similar environments are rapidly gaining AI-powered features that make working with data more accessible. As these tools evolve, so do the analytical capabilities of the people using them.

• BI tools are becoming conversational: With the rise of agentic analytics, non-technical users can increasingly talk to their data using natural language and get answers on demand.

Taken together, this doesn’t necessarily eliminate the need for data analysts, but it does raise the baseline. Many stakeholders will start operating at an entry-level analyst level, which will change both expectations and how analytical work is distributed across organizations.

If you want to get ahead, bring this with you into 2026

I know I mentioned quite a few changes coming into our field, and it might feel overwhelming to many of you, but at a high level, your strategy should be built around three areas, each helping you accelerate your career and stay relevant in the market:

1. Double down on human-centric skills
   As more of the execution gets automated, skills like judgment, communication, and the ability to turn insights into decisions will be what truly differentiate strong data scientists.

2. Focus on understanding how to build end-to-end systems
   The biggest leverage will come from understanding how data, models, infrastructure, and decision-making fit together, not just from optimizing individual pieces.

3. Don’t wait until it’s too late, start future-proofing your career today
   The gap between those who adapt early and those who wait will widen faster than most people expect.

Here is my proven roadmap to build these skills in 2026

Everything I’ve shared so far reflects clear patterns already taking shape across the field and what they imply for data scientists going forward.

Over the past year, one way I’ve personally responded to these shifts is by building and refining a bootcamp focused on agentic analytics, AI workflows, and system-level thinking. It’s designed for data scientists who want to move beyond isolated analyses and start building end-to-end systems that actually scale their impact.

Last cohort, we welcomed 22 motivated students. This is what some of the alumni had to say:

Enrollment for the January 2026 cohort is now open, but seats are limited. Use the coupon code JAN26-25-ANDRES to receive 25% off (Only 10 left):

Enroll today

Keep in mind that if you miss this cohort, the next one won’t be available until mid-year.

Thank you for reading! I hope you found this article insightful. And share in the comments if you think there are any other trends I should’ve highlighted.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

Although a lot of people still think of Colab as “Jupyter in the Cloud“, something they reach for when they need GPU power rather than a primary environment.

I genuinely think these latest updates are tipping the balance.

Colab is not only integrating much more naturally into our existing workflows, but also offering powerful features that are simply hard to match.

Let me show you exactly what I mean, and at the end of the article, I’ll share my opinion on which is my tool of choice.

Update 1: The VS Code Extension for Colab

Google released an official VS Code extension for Colab notebooks, allowing you to open, edit, and run Colab directly inside the VS Code interface, or one of its forks like Windsurf, Cursor AI, etc.

Why this matters:

• You get the full VS Code experience: richer editing, better linting, extensions, file navigation, Git integration, and more.

• The execution still happens on Colab’s backend, meaning you keep the speed and GPU access, but with a real development environment on top.

For analysts and data scientists who already use VS Code for Python development, this single update removes one of the biggest sources of friction.

Update 2: Free Access to Gemini and Gemma Models

Google Colab just announced two updates that make their generative AI features more accessible to everyone:

1. The google.colab.ai Python library is available to all Colab users, including free-tier users, which gives you access to their Gemini and Gemma models.

2. The AI Prompt Cell for a more intuitive no/low code AI workflow. It is basically a dedicated cell type designed for low-code interaction with LLMs.

Check out this notebook to get started.

Update 3: Colab Data Explorer: One-click access to Kaggle datasets and models

Kaggle and Google have collaborated to introduce the Colab Data Explorer, a new feature that allows users to seamlessly search and access Kaggle datasets and models directly within Google Colab.

You no longer have to introduce any code or leave your notebook, just browse from the sidebar, pick what you need, run the snippet, and start working.

This integration streamlines the workflow for data scientists and ML engineers by enabling them to find and incorporate relevant datasets and pre-trained models without leaving their Colab environment.

My opinion: Colab vs Jupyter

Jupyter has been my default analysis environment since I graduated from university back in 2017.

And their recent updates to their Jupyter AI extension really felt like the right move forward in keeping up with how AI-assisted coding is becoming a natural part of how we explore data, iterate on ideas, and write production-ready analysis today.

But Colab is evolving fast, and these three updates, combined with its seamless integration of Gemini chat, put it at the clear top for fast experimentation, collaborative work, and AI-first workflows without friction.

To me, the only reason Jupyter still ranks high is how naturally it fits into a properly set up data science project, where notebooks are the starting point rather than the end, with your environment, your dependencies, and no surprises when you reopen the notebook a week later.

But I’m curious to hear your thoughts…

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope you found this breakdown useful.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

Data Scientists spend a disproportionate amount of time cleaning data rather than analysing it and extracting insights

Fortunately for us, this is one area where AI can actually be extremely useful in helping automate and provide huge productivity gains.

Which is why it was one of the first things I set out to build earlier this year as I began my journey with AI workflows and agents. I built an agent to handle the repetitive grunt work of fixing data types, removing outliers, handling missing values, etc, so I could focus more time on the analysis that matters.

Today, I will show you how to build a lightweight version of this system using LangGraph, so you can start bringing automation into your own analysis workflow.

But before that, let me show you the agent in action:

Now, let’s get to the implementation…

The data cleaning workflow

Before we get to the code, let’s visualise our workflow at a high level using LangSmith Studio.

As you can see, the graph itself is actually very simple. It only contains 3 functional nodes and a single conditional edge. Here is an overview of what each one of these nodes handles:

1. create_data_cleaner_code: Call an LLM and instruct it to generate data cleaning Python code based on a series of requirements and a preview of our dataset.

2. execute_data_cleaner_code: Safely executing the Python code generated by the previous node.

3. fix_data_cleaner_code: Provide automatic error correction using an LLM feedback loop.

💡 This is the minimum functional version of our workflow. It performs well as a baseline, but it is only the starting point. You can extend it with additional elements like a human-in-the-loop stage or a more advanced planning node.

Alright, let’s see how the core logic works 👇

LLM-powered code generation node

For our purposes, this is the “reasoning“ node, the component that decides the most appropriate way to clean the data. Behind the scenes, it calls an LLM to generate Python code based on instructions and dataset characteristics.

The prompt is handled using the PromptTemplate method by LangChain, which is great for defining a prompt with placeholders and filling them before sending it to an LLM. Here are the main parts of the prompt:

• Main instructions: Define the cleaning policies the model should follow, describing how to handle common issues such as missing values or duplicates when they appear in the dataset.

• User instructions: Free-form directions provided by the user that allow the agent to customize the cleaning logic based on specific needs or preferences.

• Dataset summary: A structured overview of the dataframe, including column names, data types, and missing-value counts, which helps the model generate context-aware code.

• Python code format: A template that forces the model to return valid, executable Python wrapped inside a single function so the workflow can run reliably end-to-end.

Now, let’s see what happens once we generate code!

Want to learn how to build this and similar AI workflows?

Most data scientists know how to prompt AI tools. Very few know how to turn AI into real workflows that 10x their analysis, amplify their impact, and future-proof their career.

This bootcamp is designed with you in mind.

For example, during week 3, you not only get a proper introduction to LangGraph, but also learn how to build a Data Cleaning agent and deploy it using Docker.

Every week, you build a project, and during the last week, you build a talk-to-your-data Slackbot that even your stakeholders can use immediately.

📌 Enrollment for the upcoming cohort is now open. Save your spot before it fills up: futureproofds.com (Use coupon code JAN26–25-ANDRES for 25% off)

Executing LLM-generated code

Once we get the data cleaning Python code from the LLM, we need to execute it in an isolated namespace and catch any errors so that we can utilize the retry logic.

Here are the key parts of this code:

• Defines the function: We use the built-in Python function exec() to parse the LLM-generated code string and create the function definition in an isolated namespace.

• Retrieves function: Using local_vars.get(), we extract the newly defined function object from the namespace so it can be called.

• Executes the function: agent_function(df) runs the generated cleaning function on the actual data, returning either a cleaned dataframe or raising an error.

Let’s see what happens if the function call raises an error…

Self-correcting with error feedback

If the generated code fails, the agent automatically feeds the error message back into the LLM. This allows the model to diagnose the failure and produce a corrected version of the function without manual intervention.

• Define prompt: prompt_template.format() creates the prompt by inserting the broken code and error message into a template that instructs the LLM to fix the issue.

• Fix code: llm.invoke() sends the prompt to the LLM to generate corrected code, which is then parsed to extract clean Python from the response.

This feedback loop gives the agent the ability to iteratively recover from common errors and continue improving the generated code.

Usage example

Here is a simple example showing how to run the agent programmatically.

This pattern works well if you want to integrate the workflow directly into your existing codebase or automate it as part of a larger pipeline.

You can also build an interface on top of this, for example, using Streamlit as in the earlier demo. But the best approach depends on how you plan to use the agent.

For example, if your goal is to call it during EDA or iterative analysis, you can package the agent as a module and import it directly into a notebook. This allows you to generate cleaning functions, inspect them, and reuse them across different datasets without leaving your analysis environment.

If you want to play around with the code, here is the GitHub repo.

Final thoughts

What this agent demonstrates is not just a faster way to clean data, but a glimpse of a different way of working. We are moving from writing every transformation by hand to designing systems that generate, fix, and execute the code for us.

And when even a lightweight agent can remove hours of friction, it becomes clear how much of our time can be reclaimed for the parts of the job that actually require judgment, analysis, and creativity.

This is just the beginning, though. We can leverage AI for much more than just cleaning messy data!

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope you found this guide valuable in showing how AI can be leveraged in one of the most time-consuming parts of our workflows.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

While the spotlight for the past few years has mostly been on LLMs and diffusion models, there’s been a quiet shift happening in core data science workflows, too.

Let me break it all down for you in easy terms and help you understand what the road ahead looks like.

So, what are foundation models?

Think about traditional ML models, where each model is custom-built for specific tasks. Foundation models, on the contrary, are trained on massive, varied datasets so they can generalize.

You don’t start from scratch.

We’ve seen this change happen in NLP and computer vision. Now it’s spreading to core data science areas like time series, tabular prediction, anomaly detection, and recommendations.

Why does this matter for data scientists?

Because it shifts the way we build solutions.

Instead of training a separate model from scratch for each use case, these new models give us a starting point that’s already been trained on billions of datapoints across domains.

That means less fiddling with feature engineering, faster experimentation, and sometimes even better performance with fewer labeled samples.

Let me give you a concrete example…

Use Case: Netflix foundation model for recommendations

Earlier this year, Netflix shared how it is consolidating its recommendation stack with a foundation model trained on hundreds of billions of user interactions.

Traditionally, Netflix maintained many specialized models, each tuned for a specific surface: “Top Picks,” “Continue Watching,” “Trending Now,” and more. These models often used overlapping data but couldn’t share learnings easily, which slowed iteration and made scaling complex.

With their foundation model, Netflix takes a unified approach:

• It learns user preferences across the full platform using a self-supervised next-token prediction setup, similar to GPT.

• The model encodes entire interaction histories as sequences, using a custom tokenization process that preserves information like watch duration, device type, time of day, and more.

• Sparse attention and sliding window sampling allow it to handle long user histories while keeping inference fast enough for real-time use.

Now, every product team can:

• Generate user and content embeddings that encode long-term behavior and intent.

• Use those embeddings for things like title-to-title similarity, personalized search, or candidate generation.

• Fine-tune smaller heads for specific objectives (like ranking “Because you watched…”), without retraining the entire model.

This isn’t just about model consolidation, it also solves hard problems like cold-start. By mixing metadata-based and ID-based embeddings through an attention mechanism, the system can meaningfully represent new titles, even before anyone watches them.

Foundation models across domains (as of December 2025)

Here’s a quick overview of some notable models available today, at least the ones I was able to find:

What the current landscape tells us

From what I’ve found, most traction is happening in time series and sequential data. That makes sense since forecasting is painful to get right and even harder to scale across hundreds of products, SKUs, or sensors. Foundation models ease that pain.

Other than that:

• Tabular data is still underrepresented, but TabPFN is a promising start.

• Recommendation systems are starting to get foundation-level treatment too, especially at large tech companies with massive interaction logs.

What’s missing? Broad adoption. Many of these models are still in early-access APIs or internal use only. But the momentum is clear.

Final thoughts

You don’t need to bet your entire pipeline on these models, unless you’ve got the perfect use case, but they’re absolutely worth getting familiar with:

• Test TimeGPT or TimesFM via their APIs or SDKs

• Try TabPFN on a small tabular dataset

• Follow open-source projects like MOMENT or Moirai for broader use cases

Foundation models are moving fast. Staying close to them now will pay off later.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this gave you a clearer view of modern solutions to core data science problems.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

That’s exactly what Jupyter AI offers: a native extension that lets you write, debug, and generate code directly inside your notebook with support for most models like GPT and Claude.

In this quick guide, I’ll show you what it can do, how to set it up, and a few thoughts on when it’s actually worth using.

Give me 5 minutes, and I’ll get you up and running!

Quick overview

Jupyter AI is an open-source extension that adds LLM features on top of the standard Project Jupyter ecosystem

As of the time of writing this article, there are two ways to leverage these features:

1. The %%ai “magic” command: This allows you to prompt LLMs from inside notebook cells to generate code, explain code, fix bugs, or answer questions.

2. Jupyternaut: This is their built-in chat UI (only available via JupyterLab) that lets you treat the model as a conversational assistant. You can feed pieces of your notebook or data and ask for suggestions, code generation, summarization, etc.

Now let me show how to get started using it…

1. Installing Jupyter AI

There are two ways to install JupyterAI, depending on your preferences. Let me show you how to install them using the dependency manager pip.

Quick install (with full features)

pip install jupyter-ai[all]

This command installs both the chat extension for JupyterLab and the magic-command support.

Magic-only install (if you don’t use JupyterLab or just want inline commands)

pip install jupyter-ai-magics[all]

With this, you won’t get the chat UI, but you’ll still get the %%ai magic.

Requirements

• Python 3.9 or higher. (PyPI)

• If you want the JupyterLab UI, use JupyterLab 4.x. (Older JupyterLab 3 is supported in legacy mode but not recommended.)

Once the package is installed, you can launch JupyterLab (or Notebook/VS Code if you only installed magics). If you installed the full extension, you’ll see a small chat icon in the sidebar.

2. Connect Your Model

Before you start asking the LLM anything, you need to give it a model provider. Jupyter AI supports many: OpenAI, Anthropic, AI21, Hugging Face, Mistral, SageMaker, and more.

If you are using Jupyter Lab, you can set it via the user interface:

Otherwise, here’s what you’ll typically do via terminal if you want to expose your keys to your environment. For example, here is what that would look like when using OpenAI’s LLMs:

export OPENAI_API_KEY=”your_api_key_here”

Here is another method in case you want to keep the key contained within the notebook in a safe way:

import getpass
import os
secret_access_key = getpass.getpass(’Enter your OPENAI Key: ‘)

# Set the environment variable without displaying the full key
os.environ[’OPENAI_API_KEY’] = secret_access_key

3. Using Jupyternaut

If you installed the full extension, you’ll see a chat panel in JupyterLab.

You can ask it to:

• Explain what a code cell does

• Write new functions for you

• Debug a piece of code that throws an error

• Summarise large outputs or notebook sections

Here is a quick example:

4. Use the Magic Command %%ai

Want help inside a notebook cell directly? Use the magic command. It works in JupyterLab, Notebook, VS Code, and other IPython environments

This is what you do, specifying your model of choice, in this case, ChatGPT:

%%ai chatgpt
Explain poisson distributions

If you are asking for code and want to ensure it is directly executable:

%%ai chatgpt --format code
Write a pandas query that filters customers who signed up in the last 30 days and returns their average spend.

Here is a quick example:

5. My Take

I’ve tried a handful of AI coding assistants, and the reason Jupyter AI stands out is that it integrates directly where most data scientists actually work.

But I still think the features are too basic, and the integration needs more work to make it truly valuable.

For example, something extremely useful would be if Jupyternaut could actually scan your notebook and help you make changes or explain parts of it without requiring you to copy and paste context within every prompt:

Nevertheless, if you live in notebooks and want an AI coding companion, this is definitely worth trying.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this guide helps you streamline your analysis workflow.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

But there is a huge shift happening behind the scenes — one that will shape the future of data analytics and artificial intelligence.

It’s what many are starting to call Agentic Analytics.

You see, in 2026, we’ll see big changes in how data professionals work and all because of a little something called a “Semantic Layer“ (and shout out to Ontologies)

If you don’t know what any of those terms mean, don’t worry, you are in the right place. I plan to break it all down for you.

Just know this: If you work in data and analytics, especially as a data scientist, this isn’t something to ignore. You have a huge opportunity on your hands to get ahead of the curve and actually shape how the next generation of analysts will work.

Here is what we’ll cover

• What exactly do we mean by Agentic Analytics?

• The Semantic Layer

• Where is the industry headed? The OSI promise

• My humble predictions for 2026

What exactly do we mean by Agentic Analytics?

I think it’s probably best we start by unpacking this term Agentic Analytics.

And no, it’s not just another buzzword; it actually represents a natural evolution of how analytics is done.

You see, for years, companies tried to solve the problem of data access through self-serve analytics. Dashboards, BI tools, and data catalogs were all great ideas, and to be fair, they worked to some extent.

Features like Looker’s “Explore from here” gave non-technical teams more autonomy than ever before.

But even with all that progress, most teams still face the same bottlenecks:

• Business users depend on data teams for every new metric or definition.

• Analysts spend time jumping between tools, context, and dashboards.

• Insights still take too long to reach decision-makers.

The promise of “self-serve” analytics became more about self-serve dashboards than self-serve insights.

That’s where AI agents change the equation.

They can interpret intent, access data directly, and generate insights on demand, not in days or weeks, but in real-time.

And that’s exactly what the term Agentic Analytics captures: a new generation of analytics where intelligent systems do the heavy lifting, and humans focus on asking the right questions.

That all sounds great, but…

Yeah, I know, I’m thinking it and so are you: It can’t all be that easy, right?

You are right, and it’s not, there is one big challenge…

Even if AI agents can access your data, they still need to understand it. And that’s where most systems fall short.

Your database knows what tables and columns exist, but it doesn’t know what “active user,” “churn,” or “retention rate” actually mean for your business.

That knowledge lives in people’s heads, scattered documentation, or buried inside Slack threads that no one will ever find again.

In other words, your data has structure, but your meaning doesn’t.

Enter: The Semantic Layer

A semantic layer creates a shared understanding of how data connects to business concepts.

It defines things like metrics, relationships, and entities in a consistent, machine-readable way.

What you’ll often find is that this layer is made up YAML files containing a structured definition of your tables and views. Here is a generic example:

So instead of an AI agent guessing what “daily active users” means, it can look it up and query the right data (with the correct metric calculations) automatically.

A brief history lesson

I need to do this short segue because I know many of you who’ve been in the data space for a while are probably screaming at your screens, saying: This isn’t anything new!

And you’d be right!

We’re talking about this now because these concepts started spiking in popularity again in early 2025 for a few reasons: mainly the rise of AI agents, context-aware systems, and the push toward standardization through initiatives like OSI (more about this in the next section).

But the idea of the semantic layer in data analytics has actually been around since the 1990s. It first appeared in enterprise data warehouses as a way to translate raw tables into business-friendly views, something business users could actually understand.

Looker later revived this concept in a modern way with LookML, a true modeling layer that defined metrics, joins, and relationships in code. That approach became the foundation for how many teams think about semantic layers today.

What’s different now is that the same concept is being extended beyond BI. Instead of only powering dashboards, semantic layers are becoming the backbone of AI agents, letting them reason about data using shared business definitions.

Where is the industry headed? The OSI promise

If there’s one thing history teaches us, it’s that every major shift in data has come from standardization.

The modern data stack gave us standard ways to store and transform data. Now, we’re moving toward standard ways to describe and share meaning across tools.

That’s exactly what’s happening right now with the Open Semantic Interface (OSI) initiative.

In September 2025, many of the biggest names in analytics and AI came together to announce a shared goal: creating an open standard for how systems define and communicate business context.

We’re talking about companies like dbt Labs, Snowflake, Google, Cube, Transform, and others, all aligning behind a common specification.

The idea is simple: instead of every platform having its own definition of metrics and entities, the OSI provides a single shared interface that any tool or AI agent can understand.

If it works, the implications are huge:

• Any AI tool (like Cursor or Claude) could plug into your company’s semantic layer.

• Metric definitions become reusable across dashboards, notebooks, and agents.

• Context becomes portable instead of something that gets lost every time you switch tools.

If the idea of keeping your business context vendor-agnostic resonates with you, this should give you real hope for the future.

My humble predictions for 2026

Here’s where I think things are headed:

1. The commoditization of the semantic layer

   By the end of 2026, the idea of a semantic layer will start moving from cutting-edge to expected. It won’t be everywhere yet, but more companies will begin adopting it as part of their modern data stack, whether through open standards like OSI or proprietary versions built into existing analytics and AI tools.

2. Agentic Analytics in practice

   We’ll also see the concept of Agentic Analytics take real shape, not as isolated AI add-ons, but as part of how data work gets done day-to-day. Expect more “talk to your data” capabilities across both lightweight integrations and enterprise platforms.

Here’s my recommendation to data scientists: be part of that design process.

You already understand how data connects to business value, which makes you uniquely positioned to shape this new layer of intelligence. Don’t wait for vendors or AI teams to define it for you.

Experiment, build prototypes, and think about how context, governance, and reasoning can live inside the tools you already use.

This is why learning how to use the semantic layer inside real AI workflows has been a core part of my AI Workflows Bootcamp.

The 22 data scientists enrolled learned to build everything from lightweight data cleaning agents to MCP servers from scratch, custom GPTs, and their own talk-to-your-data Slackbots.

👉 If you’d like to join Cohort 2, you can add yourself to the waiting list at futureproofds.com

Closing thoughts

There is no doubt in my mind that we’re entering a new phase in analytics.

For years, data scientists have focused on building models, uncovering insights, and helping teams make better decisions. That work isn’t going away. What’s changing is how we get there and how quickly we can democratize insights.

The rise of semantic layers, OSI, and Agentic Analytics marks a shift toward systems that can understand context and reason about data more like we do.

If you’ve read this far, take it as your cue to start exploring what’s possible.
Because the data scientists who learn to work with these new systems early on will be the ones shaping what analytics looks like in the years ahead.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this article helps you look ahead and prepare accordingly

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

Until recently, that wasn’t easy. Giving these tools access to live data or metadata required custom APIs, wrappers, or endless prompt engineering.

That’s where MCP changes everything; it standardizes how AI environments connect to external systems like Postgres, MySQL, BigQuery, and MongoDB.

In this guide, I’ll show you how to quickly set up and configure an MCP server for your database so you can query and analyze data directly from your AI workspace.

This will be a technical walkthrough, but it’s much simpler than you might expect.

Let’s get to it!

First, what is MCP?

At its core, MCP (Model Context Protocol) is a framework that standardizes how AI agents connect to external systems such as databases, APIs, file stores, or any tool that holds useful context.

You can think of it as a structured alternative to APIs, but designed specifically for AI workflows. Instead of every tool exposing a unique REST endpoint and every agent writing custom integration code, MCP defines a consistent protocol for discovery, messaging, and execution.

This is why there now exist so many MCP server directory sites where you can find MCP servers that others have built for different purposes, and that you can easily download and use.

Although you can probably find an MCP server for your database in one of these directories, I’m going to show you a better alternative…

MCP Toolbox for Databases

MCP Toolbox for Databases is an open-source framework developed by Google that automatically spins up an MCP server for your database (Postgres, MySQL, BigQuery, MongoDB, etc.) using a short YAML configuration.

The main advantage of using this tool is that it already takes care of most of the boilerplate code needed to expose your database through MCP. It comes with several built-in tools that cover the most common use cases out of the box:

• list_tables: Lists all tables in the connected database.

• execute_sql: Executes SQL queries and returns results in a structured format.

• list_active_queries: Displays currently running queries for monitoring and debugging.

…and many more, depending on the database type.

And if you need additional functionality, you can easily define your own tools by adding just a few lines to a tools.yaml file. This gives you the flexibility to extend or override the default behavior without writing server code from scratch.

Let me show you a few examples of how you can start using it…

Setting up a Postgres MCP server with Cursor AI

Installing this toolbox on macOS or Linux is as easy as doing:

brew install mcp-toolbox

For additional instructions, check out the documentation.

Let’s say you have a Postgres database and want to connect it to Cursor AI so you can read data, write queries, and run them without ever leaving your workspace

Here is all it takes to do that:

Step 1: Click on “Add Custom MCP“ in the Tools & MCP tab of your Settings

Step 2: Add your Cursor MCP configuration

This mcp.json file tells Cursor which MCP servers to start (or connect to) when it launches. In this example, all you need to do is replace the “command“ key with the path for toolbox, and fill in your database credentials.

That’s it! If you see this, then your MCP server has been successfully set up

This is a quick preview of how you can call your tools from a chat:

By the way, here is the mcp.json configuration so you can easily copy and paste it:

{
  “mcpServers”: {
    “postgres_toolbox”: {
      “command”: “./PATH/TO/toolbox”,
      “args”: [”--prebuilt”, “postgres”, “--stdio”],
      “env”: {
        “POSTGRES_HOST”: “your-host”,
        “POSTGRES_PORT”: “5432”,
        “POSTGRES_DATABASE”: “your-database”,
        “POSTGRES_USER”: “your-username”,
        “POSTGRES_PASSWORD”: “your-password”
      }
    }
  }
}

Creating additional tools with tools.yaml

Now, let’s assume that you need tools that aren’t already provided by default. To do that, you can easily define your desired tools inside a tools.yaml file.

In this example, I create two tools based on common actions I want to create a shortcut for:

1. get_weekly_active_users

2. get_database_size

Now, if you wanted to connect this server to Cursor AI, this is what the mcp.json file would look like:

{
  “mcpServers”: {
    “postgres_toolbox”: {
      “command”: “./PATH/TO/toolbox”,
      “args”: [
        “--stdio”,
        “--tools-file”,
        “./PATH/TO/tools.yaml”
      ],
      “env”: {
        “POSTGRES_HOST”: “your-host”,
        “POSTGRES_PORT”: “5432”,
        “POSTGRES_DATABASE”: “your-database”,
        “POSTGRES_USER”: “your-username”,
        “POSTGRES_PASSWORD”: “your-password”
      }
    }
  }
}

How am I using MCP servers in my workflow?

At work, I’ve been using the BigQuery MCP Server (built using the toolbox) to connect Cursor directly to my data.

Most of my coding happens in Cursor anyway, and so when I do data analysis, instead of switching tools to write my queries and bring in my data, I can now do everything I need inside Cursor.

I also recently added the Confluence MCP server, which is handy when I need quick access to documentation or notes while I’m exploring data.

It’s a small change in setup, but it’s made a big difference. Everything I need, from code to queries to context, now lives in one workspace.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this guide helps get the most out of your AI tools.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

Last week, I attended the Nordic DS/ML Summit 2025 in Stockholm 🇸🇪, a two-day event bringing together industry leaders, researchers, and practitioners to share how data science and machine learning are evolving.

The lineup included big names like Meta, NVIDIA, and Google DeepMind.

And of course, the hottest topic was….🥁… Agentic AI (no surprise there)

This photo is from an interesting panel discussion featuring:

• Chi Wang, Senior Staff Research Scientist at Google DeepMind and Founder of AutoGen (now AG2)

• Rohit Patel, Director at Meta Superintelligence Labs

• Sepideh Pashami, Acting Director of Data Analysis at RISE Research Institutes of Sweden

So I would say yes, it was definitely time well spent!

Today, I’m going to break down my learnings, the key trends I observed, and, towards the end, tie it all back to what we, Data Scientists, should pay attention to as we enter 2026.

Let’s get to it!

3 key trends that stood out (to me) the most

These are the trends that stuck with me during the event, and that, in some way or another, have gained enough momentum to be worth following closely.

1. From traditional analytics to agentic analytics

We’re entering a new phase of analytics. One where dashboards and static reports aren’t enough anymore.

I foresee this will become an even bigger topic by the beginning of 2026.

The focus will be on creating more dynamic systems that can speed up the path from data to insight, making analytics more adaptive and less dependent on manual exploration, which many are starting to refer to as Agentic Analytics.

💡 One thing is clear, the companies who will lead the way in this Agentic Analytics shift, will be those who have the foresight to build a strong data engineering foundation and invest in their semantic modeling. That’s ultimately what will enable AI agents to interact with their data in meaningful ways. More on this later in the article.

2. Small language models are the next big thing

Small language models (sLMs) are becoming surprisingly capable.

Models like Phi-3, Mistral, and Llama 3 8B show that you don’t need massive infrastructure to get strong performance. With some fine-tuning, they can even outperform larger models on focused tasks.

💡 For developers and smaller teams, this also means we can now run fast, private, and low-cost models, on a regular laptop or even a phone.

3. The rise of specialized multi-agent systems

A big theme that kept coming up was the move toward hierarchical multi-agent systems. Instead of relying on a single agent to handle an entire workflow, newer architectures now use an orchestrator agent that breaks tasks into smaller pieces and delegates them to specialized sub-agents.

Each sub-agent focuses on one tiny, well-defined task, like cleaning data, summarizing findings, or generating code, and becomes extremely good at that single thing. Together, they form a coordinated system that’s faster, cheaper, and more reliable than one general-purpose agent working alone.

💡 This “divide and conquer” approach also opens the door for sLMs to play a bigger role. Since each sub-agent only needs to handle a narrow task, even lightweight models can perform well when combined in a well-orchestrated system.

It’s a design pattern we’ll likely see more of as agentic systems mature and move into production use.

What should Data Scientists pay attention to?

My recommendation, especially to mid-senior data scientists who want to take the next leap in their career growth: Lead the way in the Agentic Analytics shift at your company.

From what I’ve seen, most organizations are still just waking up to this change.

That means you have a real opportunity to lead, whether it’s by championing modern enterprise tools that enable agentic analytics or by building your own agents that make analysis faster, more interactive, and closer to decision-making.

The people who learn to bridge that gap early, between AI agents and analytics, will be the ones shaping how Data Science is practiced in the next decade.

5 real-world agentic AI use cases in Data Science

These are some use cases where GenAI and Agentic AI are already starting to make an impact:

1. Conversational dashboards for fast insights
   Think dashboards you can talk to, ask questions in plain English, and get instant summaries or visualizations. Power BI Copilot and Tableau Pulse are early examples, but the concept applies to any workflow where non-technical users need fast answers from data.

2. EDA and data cleaning agents
   AI agents are starting to automate the time-consuming work of detecting outliers, normalizing data, and generating initial visualizations during EDA. Tools like Tableau’s Data Pro hint at how you can build agents to accelerate your data prep process.

3. Foundation models for analytics
   Instead of training a new model for every metric or product, foundation models like TimeGPT are starting to handle forecasting, anomaly detection, and other analytic tasks straight from raw data. This makes advanced analytics more accessible, even for teams without deep expertise in time series or model building.

4. Agentic monitoring and proactive analytics
   Rather than waiting for a human to check a dashboard, agentic systems can watch KPIs, detect shifts, and trigger alerts or recommendations. Tableau Inspector and Adverity are pushing this, but it’s a pattern any analyst can explore with the right setup.

5. Multi-agent orchestration of the ML workflow
   Platforms like causaLens are an example of what’s possible with AI agents that collaborate, some clean the data, others build models, and others explain results. It’s not just automation, it’s coordination, and it’s a glimpse of how future ML workflows could run.

🔑 Don’t ignore this: The Semantic Layer

I wanted to mention this concept again since I feel it might get overlooked by a lot of you reading this article, and that would be a BIG mistake.

Over the past 6 months or so, I’ve been spending more time building my own AI workflows to optimise and automate much of my data science work. And recently, I deployed a talk-to-your-data Slackbot that is slowly redefining the meaning of self-serve analytics at my company.

One of the keys to the success of these tools has been defining the semantic layer.

📌 This is also why I’m teaching it to the 22 Data Scientists currently enrolled in my AI Workflows bootcamp.

The idea is simple: the semantic layer creates a shared definition of metrics and business logic so data scientists, stakeholders, and most importantly, AI agents, all work from the same source of truth.

And believe me, you don’t need to be a data engineer to start building a semantic layer to enhance your AI agents.

Honorable mentions

Here are some cool tools and products I didn’t get to cover, but thought deserved at least a quick mention:

• Orion: An on-device autonomous agent that orchestrates your AI tools and runs locally for cross-platform automation.

• AG2: Open-source framework (formerly AutoGen) for building and orchestrating multi-agent systems.

• HP ZGX Nan AI Station: A nano AI workstation for prototyping and fine-tuning inference models of up to 200B parameters locally.

Closing thoughts

If there’s one thing this year’s summit made clear, it’s that the gap between AI research and real-world data science is closing faster than ever.

Agentic systems, small models, and better data foundations aren’t just trends; they’re signals that our field is evolving into something more intelligent.

For data scientists, this next phase isn’t about chasing every new tool that comes out. It’s about understanding the direction we’re heading and learning how to apply these ideas where they actually create impact.

If you do that, if you stay curious, experiment with these concepts, and think beyond your notebook, you won’t just keep up with the changes. You’ll help shape what data science looks like in 2026 and beyond.

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope these insights give you a competitive advantage as we prepare to enter 2026.

- Andres Vourakis

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

When I first started this newsletter back in 2024, data science was already changing fast.

But back then, the biggest question most people seemed to have was how to land a job.

So that’s mainly what this newsletter focused on: resumes, interviews, portfolio projects, and everything else you needed to navigate the job market effectively.

Since then, this community has grown to almost 13K data scientists from all over the world and at different levels of their careers.

But the field keeps evolving, and so does this newsletter

Many data scientists today are asking new questions:

• What skills will still matter two years from now?

• How do I stay relevant when AI can do half my work?

• How do I grow when the tools, expectations, and roles keep changing?

That’s exactly what this new chapter is here to help you with.

Introducing Future Proof Data Science

Starting today, To Be a Data Scientist is becoming Future Proof Data Science, a place for data scientists who want to grow in their careers and stay ahead as AI, new tools, and shifting expectations redefine the field.

What to expect…

You joined this newsletter to get value in the form of weekly practical insights, and that promise is not changing one bit. But moving forward, these are the types of articles you’ll find in this newsletter:

• Practical ways to integrate AI into your daily data science workflows without overcomplicating your process.

• Deep dives into the skills and tools that will shape the next generation of data scientists.

• Discussions about mindset and judgment, what separates good data scientists from great ones.

• Clear takes on what’s lasting vs. what’s just trending, so you can invest your time where it matters most.

💡 It’s more than a newsletter: You should also know that although I’m bringing you weekly articles, Future Proof Data Science is also a broader initiative designed to help data scientists stay relevant in the age of AI. By being part of this community, you’ll get early access to workshops, hands-on bootcamps, and real-world examples that help you apply what you learn right away.

🙌 Welcome to the next chapter!

For those of you who are mid to senior data scientists, you’ll get the most value from the deeper discussions on strategy, automation, and AI-driven workflows, the things that help you work smarter and make a bigger impact.

But even if you’re a junior or aspiring data scientist, don’t worry, you’ll still find plenty here to guide your growth and prepare you for what’s coming next.

That’s all for now, see you next Thursday!

Andres Vourakis

This is the reaction every stakeholder has after you announce you built yet another great ML model, but there are no plans to move it out of its Jupyter notebook.

They are dying for you to answer, “How can I actually use it in my day-to-day?“

The future of data science isn’t just in building models or running analyses. It is in making sure your work can be reproduced and actually used by others:

• If your notebook only runs on your laptop, it does not create a lasting impact.

• If your model cannot be shared, it never reaches its potential.

As the field continues to move from individual experiments to collaborative projects and production systems, these challenges matter more than ever.

Reproducibility and accessibility are no longer nice-to-haves. They are the foundation of professional data science.

This is where Docker comes in. It gives you a way to package your work so it runs the same everywhere and can be shared without the usual dependency headaches.

So if you give me 5 minutes, I promise to get you started using Docker.

Here’s what we’ll cover:

• Why Docker matters for Data Science

• Docker Images vs. Containers

• Dockerfile basics

• 🎥 Docker in action demo

Why Docker matters for Data Science

In data science, two challenges come up again and again:

1. Reproducibility: making sure your code runs the same way today, tomorrow, and on someone else’s machine.

2. Accessibility: making your work usable by others, whether that is a stakeholder opening a dashboard or a teammate testing your model.

Docker tackles both.

By packaging your environment into a container, you avoid the dependency issues and version conflicts that often break projects. Once your work is in a container, it is no longer tied to your setup. It can be shared, deployed, or scaled without worrying about the hidden details of your machine.

Here are three examples that show what this looks like in practice:

1. Jupyter Notebook Environment

   Spin up a ready-to-use JupyterLab with Python and all your favorite libraries pre-installed. Instead of fighting with conda or pip, you and your teammates can share the same reproducible setup with one command.

2. Machine Learning Model API

   Package a trained ML model (like a churn predictor or recommendation engine) inside a Flask or FastAPI container and expose it as an API. This makes your model available for real-time predictions that other apps, dashboards, or teammates can call directly.

3. Streamlit Dashboard

   Wrap your Streamlit app inside Docker so it can be deployed anywhere with zero setup. Great for sharing interactive dashboards or prototypes with stakeholders without worrying about their local Python setup.

Docker Images vs. Containers

A common source of confusion when learning Docker is the difference between an image and a container.

Think of it like this:

• An image is a blueprint. It defines everything your project needs, such as code, libraries, and environment setup, but it does not run by itself.

• A container is a running instance of that blueprint. It is what you can actually interact with, stop, or restart.

In practice, you only need to build an image once, and you can run as many containers as you want from that image

Dockerfile basics

A Dockerfile is the recipe that builds your image.

Once you have an image, you can start containers that run your work the same way on any machine.

1. Project setup

Your Dockerfile goes in the project, usually in the root directory

project/
  app.py
  requirements.txt
  Dockerfile

2. Write your Dockerfile

Here is a template you can start from:

What each line means:

• FROM: chooses the base image (a lightweight Python in this case).

• ENV: sets flags that reduce clutter and keep logs visible.

• WORKDIR: defines the working directory inside the image.

• COPY requirements.txt + RUN pip install: installs dependencies in a cacheable layer.

• COPY . .: copies your project files.

• CMD: tells Docker what to run when the container starts.

4. Build the image

From the project root, run:

docker build -t my-app:latest .

5. Start a container

Run your app with:

docker run --rm my-app:latest

If your app serves HTTP, map a port:

docker run --rm -p 8000:8000 my-app:latest

🎥 Quick demo

Here is a quick walk-through of how to build a Docker image and run a container:

💡 Pro tip: Add a .dockerignore file

Keep builds fast and images small by creating a .dockerignore file in your project.

This file tells Docker which files and folders to skip when copying your code into the image. Without it, everything in your project directory is included by default, which can bloat your image with things like cached files, large datasets, virtual environments, or even your entire .git history.

A good starting point:

__pycache__/
*.pyc
*.ipynb_checkpoints
.env
.venv/
.git/
data/

This way, your image contains only what it actually needs to run, nothing more. It speeds up the build process, keeps images lighter, and avoids leaking sensitive or irrelevant files.

Wrapping Up

By now, you’ve seen how Docker helps with the two things data scientists often struggle with most: making work reproducible and making it accessible.

We looked at real examples where Docker fits into everyday projects, cleared up the difference between images and containers, and built a foundation with the Dockerfile.

This is enough to get you started, but it’s only the beginning.

In Part 2, we’ll go a step further:

• Docker compose files

• Layer caching

• Sharing images on Dockerhub and more

A couple of other great resources:

• 🚀 Ready to take the next step? Build real AI workflows and sharpen the skills that keep data scientists ahead.

• 💼 Job searching? Applio helps your resume stand out and land more interviews.

• 🤖 Struggling to keep up with AI/ML? Neural Pulse is a 5-minute, human-curated newsletter delivering the best in AI, ML, and data science.

Thank you for reading! I hope this guide helps you start deploying your projects.

See you next week!

- Andres

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!

📣 Today’s post is brought to you by a special guest, my friend Penelope Lafeuille from . She is a Senior Data Scientist based in the US who writes about landing data science jobs and getting promoted. I highly recommend you check out her work.

Two years ago, I thought I deserved to be promoted to “Senior Data Scientist”.

• I was working hard,

• delivering value,

• hitting deadlines…

Yet nothing happened.

It stung.

And it wasn’t because I lacked effort or skills (I was an A+ student).

I got promoted this year—but only after I fixed three mistakes that were holding me back.

• I wasn’t working on what truly added value to the company.

• I slacked on keeping my manager updated (even if I was going above and beyond…)

• I never actually asked for a promotion.

If this sounds familiar, don’t worry.

I’ll share what I changed and how you can apply it to accelerate your own career growth.

Mistake 1: Not working on what truly adds value.

For a long time, I got lost in what I thought mattered—cleaner models, fancier dashboards, sharper code.

But promotions don’t come from technical polish alone. They come from business impact.

I had to ask myself:

• Which problems keep my manager awake at night?

• Which projects move revenue, save costs, or influence key decisions?

Once I aligned my work with those levers, everything changed. Suddenly, my efforts weren’t just “good work”—they were critical work.

If you’re not sure what those levers are, ask. A simple:

“What’s the most important problem I can help solve this quarter?”

…will put you ahead of most peers who just wait for tasks to roll in.

Mistake 2: Slacking on my updates.

Out of sight, out of mind.

I thought my boss would notice my hard work. Instead, weeks of progress went unseen.

Think about Pizza Hut’s order tracker. The pizza doesn’t cook faster, but seeing “Prepping → Baking → Out for delivery” makes the wait easier.

Your boss needs that same visibility.

Pro tip: send a 5-minute daily recap.

At the end of the day, drop a quick Slack message:

• What you worked on

• What you shipped

• What’s next

It feels small. But over time, those daily updates become a powerful paper trail of your contributions. When promotion time comes, you’re not scrambling for proof—you’ve already built it.

Mistake 3: Not asking for a promotion.

I used to think that if I just worked hard enough, someone would notice.

But managers aren’t mind readers.

It’s not just about asking for a promotion. It’s about opening a dialogue—saying:

“I want to add as much value as I can to the company. I’ve already been doing that, and I believe I can do it even better if I get promoted.”

But don’t move to this step until you’ve done the groundwork:

1. Sent consistent daily recaps to make your progress visible.

2. Focus your efforts on projects that clearly add business value.

Once those are in place, the conversation becomes a natural next step—not a gamble.

Simon Sinek nailed it:

“These conversations go south when they become a demand instead of a dialogue.”

That was my problem. I wasn’t having any conversations at all.

Here’s the framework I now use:

1. Ask for permission first

Don’t ambush your manager. Say:

“Could we set up a time to talk about how I can keep contributing more to the company and how my growth and compensation could reflect that?”

2. Lead with commitment

“I really enjoy working here, and I want to keep growing with the company.”

3. Make it about the future

“I’ve taken on more responsibilities, and I’d love to understand how my compensation could reflect that. What goals should I focus on to continue growing with the team?”

See the shift? It’s not a demand. It’s a roadmap.

I didn’t get promoted for two years—not because I wasn’t good enough, but because I wasn’t playing the right game.

Once I started:

• Aligning my work with what the company values most,

• Making my progress visible every single day,

• Asking for promotions instead of waiting for them,

…I finally broke through.

And you can too.

So here’s my challenge for you this week:

1. Identify one project that clearly ties to business impact.

2. Send one daily recap—for five days straight.

3. Write one respectful sentence you could use to ask your manager for a growth conversation.

Do that consistently, and your next promotion won’t be a question of “if”—only “when.”

Until Next time,

— Penelope

Before you go, please hit the like ❤️ button at the bottom of this email to help support me. It truly makes a difference!