No Money or Job Offers: My Unusual Path to Data Science

Opening your own doors

If you’ve been reading my work long enough, you’ll know that I didn’t break into Data Science directly.

I took the scenic route (aka started as a Data Analyst).

But something you definitely won’t know—because I’ve never shared this story before

Is that I didn’t even break directly into Data Analytics right after University

This is the story of how I turned a non-technical job into a career in Data Science, along with key lessons that have stayed with me over the years, helping me advance my career and create new opportunities in my life.

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Chapter 1: No Money or Job Offers

I didn’t attend my graduation ceremony.

For me, closure came after going into my LinkedIn profile and adding another job to my list of experience.

Landing a “Machine Learning Engineer” internship right before graduation meant everything to me. It was the first sign that after 5 arduous years in school, my degree—which I never picked up— was actually worth something.

The only problem though…It didn’t pay my bills

As a broke university graduate, what do you do when you can't afford to wait through endless interviews and grueling technical challenges, hoping for a job offer—while rent is due at the end of the month?

Well, that’s how I ended up working from the backroom of a retail store 5 days a week

Answering phone calls all day, repeating “Gear Coop customer service, how can I help you?“ more times than I can remember

And replying to emails from customers who had clearly over used our products, but still demanded a full refund.

Oh and also moving boxes from time to time.

Chapter 2: The opportunity

Honestly, I didn’t expect to like my customer service job—but I did.

The work wan’t very hard and since I love the outdoors, working for an outdoor retail store surrounded by like minded people made my day-to-day quite enjoyable.

But there was one thing that drove me crazy: the repetition.

Coming from a tech background we are wired to think about efficiency. So I couldn’t stand doing mindless repetitive tasks day after day.

One of those tasks? Manually downloading reports from Amazon Web Services.

So, I gave myself a side project:

Write a Python script to connect with the Amazon API and schedule those report downloads.

That one small project changed everything for me.

Chapter 3: The value proposition

What seemed so trivial to me was actually going to save us close to 100 hours a month.

And that was just one small part of the many bottlenecks we had in the company.

That’s when I knew, and so did my manager, that I could offer a lot more business value than simply answering calls and handling return cases.

The company already had a small team of three data analysts handling all kinds of tasks, from simple Excel reporting to supply chain optimization.

But the team wasn’t very technical

And given my background, I knew I could help solve two big pain points:

1. Automating manual, repetitive tasks.

2. Uncovering new insights from their data

So, I scheduled a meeting with the CEO to pitch myself as a potential new addition to the data team.

Chapter 4: The Ask

I sat down in that meeting for an hour, talking about my background, my internship as an ML Engineer—yes, I was still doing that on the side—and most importantly, the value I could provide.

I was nervous, and the more I worried about whether it was showing, the more nervous I became.

I knew I had something to offer, but I wasn’t sure the CEO would see it that way. And he was clearly skeptical, questioning my ideas and pressing to make sure I would actually be worth the investment.

Luckily, I came prepared. I spent time shadowing the data team, which helped me explain exactly where my automation and analytical skills could make a difference.

And even though I was inexperienced and “quantifying impact“ was something I was just getting familiar with, I tried to make some educated guesses.

Frankly, I was so nervous that I felt I was blowing it.

But by the end of the meeting, we shook hands, and I walked out with a new job title and a pay raise.

That meeting with the CEO didn’t just get me a new title, it was the beginning of my professional journey into Data Analytics, and it’s how I eventually became a Data Scientist.

Allowing me to do more of the kind of work I’m passionate about

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My Biggest Takeaways

1—You are responsible for your own growth

Don’t ever wait around for managers, leads, or peers to guide you or push you forward.

Whether it's learning a new skill, taking on more challenging tasks, or simply finding a more efficient way to do your current job.

Don’t wait for someone to hold your hand and guide you. Take charge of your path!

2—It all comes down to the value you can provide

No one cares about your title as much as they care about the results you deliver.

What truly matters is the value you bring—whether it's improving a process, solving a tough problem, or finding a new way to make things work better. Focus on making a meaningful impact, and people will take notice.

The value you provide speaks louder than any role or label.

3—Opportunities come when you ask for them

Many of the opportunities I’ve had came from simply asking.

When I requested that meeting with the CEO, I didn’t know how it would turn out. But asking for the chance to show my value opened the door to a new role.

Don’t hesitate to ask for more responsibility, to shadow a team, or to pitch a new idea.

You’ll never know what doors can open until you take the first step.

My story is not unique…

I went into Reddit to ask people to share their story of how they successfully transitioned to a data position within the same company from a non-data related role.

I got over 30 inspiring success stories:

• Customer service representatives (like me) who automated tasks and worked their way into data roles.

• Fraud specialists who applied their insights to data analysis and strategy.

• Marketing professionals who leveraged their skills with campaign analysis to move into data-driven roles.

• IT and web developers who transitioned from technical support to data engineering and analysis.

Read all of the stories in this reddit thread

My goal with this article isn’t to suggest you take a customer service job as a way to break into Data Science, but rather to help you keep your eyes open to other alternative paths.

Especially for those of you switching careers—don’t overlook your current company. Moving internally is often much easier than breaking into the field with a new company.

But most importantly, my goal was to remind you that you are in charge of creating your own opportunities.

Don’t wait around for opportunities to come to you, identify where you can create value and don’t be afraid to ask for what you want.

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A couple of great resources:

• 💼 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.

• 🤝 Want to connect? Let’s connect on LinkedIn, I share lots of career bite-size advice every week.

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Thank you for reading! I hope you enjoyed this slightly less technical article.

See you next week!

- Andres

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