A computer science degree, an MBA, and a law degree in data privacy — not because I couldn’t decide, but because each one answered a question the last one raised. Somewhere in between, I discovered how much I loved automating the way work gets done — a thread that runs from a bank in Ukraine to the multi-agent AI systems I build today.
My name is Anastasiia. I was born in Ukraine, where I earned a Master in Computer Science with a specialization in statistics, then went on to complete an MBA with a focus in international economics. I worked in the financial industry, analyzing small businesses at a bank — I wanted the work to actually make a difference for the people on the other end of it, not just produce a report. I ended up automating parts of that work myself, which is when I discovered how much programming could transform the way work actually got done.
I moved to the United States in 2013. The biggest surprise wasn’t the culture or the language — it was the legal system. I couldn't make sense of my own paperwork. I remember not fully understanding what I was agreeing to just to see a doctor. So I went back to school, this time to Loyola for a Master of Jurisprudence in Data Privacy and Security — not to practice law, but to understand, formally, how data, consent, and legal risk actually connect.
I worked as a financial analyst at a few companies after the move, then decided to change the scenery entirely and went into e-commerce. That’s where something clicked. I loved investigating customer behavior — not just what people bought, but why — and I loved automating the systems around it even more. I built frequently-bought-together and recommendation models.
The two things I cared about most, understanding people and building systems that scale that understanding, turned out to be the same job.
As AI capability grew, I leaned into it — not as a pivot, but as the next version of work I was already doing. Today I build multi-agent AI systems, and the same questions still drive it: what does this data actually mean, what happens when the system gets it wrong, and how do I build something people can actually trust.