About

Building practical data science on top of enterprise business experience.

Enterprise financial data taught me what makes a number trustworthy. An MSc in Data Science taught me how to build the systems that produce it.

Backend Financial Analyst — State Street

Worked with enterprise financial data — reporting, reconciliations, controls, and KPI tracking. The role wasn't data science, but it's where I learned what it actually takes for a number to be trusted.

Logistics Operations — Danish Crown

Worked with operational data in SAP, focused on process improvement across logistics workflows. Different domain, same underlying skill — finding where a process breaks down and using data to fix it.

Career transition — MSc Data Science

Went back to study the technical foundations — statistics, machine learning, data engineering — needed to turn the analytical instincts from finance and operations into models and systems.

Why this background matters

Coming from finance and operations rather than a pure computer science path changes how I approach a modeling problem: I start with the business question and the decision the output needs to support, not with the most interesting algorithm available.