My Path to Data Science and Credit Scoring

I’ve been thinking about coming back to this blog for a while.
At some point, I realized that I’ve accumulated enough experience, questions, and small insights that are worth putting into words - not as final answers, but as part of an ongoing process.

My path into Data Science wasn’t linear, and for a long time I thought that was a disadvantage. Now I see it differently.

I have a background in economics and started my career in a bank, working in an operational role. It wasn’t glamorous, but it gave me something very valuable - an understanding of how things actually work in practice: processes, constraints, and real customer interactions.

Later, I moved into sales and eventually became a team lead. That experience shaped me in a different way. It taught me how to work with people, how to make decisions without having perfect information, and how to see what’s behind the numbers.

After relocating to a new country, I had a chance to rethink my career path. That’s when I decided to move into Data Science.

It wasn’t an instant transition. There were courses, projects, and internships - including work in computer vision with Finnish companies. It was a period of intensive learning, with its fair share of uncertainty, but also a lot of growth.

One of the most valuable parts of that journey was being surrounded by people who were willing to share their knowledge. I’ve been lucky to learn from experienced professionals, and that has made a huge difference in how I approach problems today.

Now I work on credit scoring problems, and in many ways it feels like a natural intersection of everything I’ve done before. I’m also lucky to be part of a team where knowledge sharing and support are part of the culture - it makes a real difference when you’re working on complex problems.

Data Science here is not abstract - it’s directly connected to business decisions, risk, and real-life outcomes.

What makes this area especially interesting to me is the balance it requires. Credit scoring is not just about building models. It’s about trade-offs, constraints, and responsibility. And, at some level, it’s also about fairness - or at least about trying to get as close to it as possible in systems driven by data.

This blog is my way of documenting that process - small observations, lessons, and questions that come up in day-to-day work.

Let’s see where it goes.