Junior Data Analyst
Amsterdam
An engineering background paired with modern data and AI tooling. I turn messy, scattered data into decisions teams can act on.
A real business result, not a course exercise — the problem, what I did, and the measurable outcome.
A high defect rate (40%) on the production line was driving up costs and waste.
Ran an analytical root-cause investigation of the process and the data behind it, then validated the fix against the numbers.
Defect rate cut from 40% to 2%.
From the classics to a modern AI stack. AI is the part I genuinely enjoy — and what sets me apart from a typical junior.
What sets me apart as a junior — building with modern AI tools, not just reading about them.
My first profession is engineering: I graduated from Odesa National Polytechnic University, in computer engineering. That is the lens I bring to any dataset — I see it as a system with a structure and an internal logic.
Moving into analytics was a deliberate choice, and I keep learning on my own. I'm especially drawn to modern AI tools — Claude Code, RAG systems, agents. I don't just analyse data: I value time, which is exactly why I keep growing my automation skills.
Beyond data, I'm curious about psychology, TRIZ (the theory of inventive problem solving), prompt engineering and AI-assisted development. These aren't random hobbies — they're how I keep my thinking flexible and look at a problem from several angles.
I do my best work on things that matter to me: human health, education, personal development and philosophy. In these areas I want to dig deeper — the data is usually there for real decisions, not just for a report.