Data Science Manager · Warsaw
I’m a Manager of Data Science in credit risk at Allegro Pay, where I lead modelling work for one of Central Europe’s largest consumer‑finance platforms. My focus is application scoring — PD models for individual borrowers — and, increasingly, what trustworthy machine learning looks like in a regulated domain: fairness, calibration, explanation.
I’ve spent close to a decade in credit risk and data science, across Allegro Pay, Hexaware (for AXA), PKO Bank Polski, and ING Bank Śląski. Before that I was a robotics engineer at Samsung and a UAV constructor at Aviation Technik. Robotics was where I started — it’s still how I think about machine learning: a system you can describe, debug, and put under load.
Outside work I learn Czech, follow Formula 1, and tinker with robotics.
Supervised by Prof. Aneta Ptak‑Chmielewska. The thesis develops an integrated methodology that combines algorithmic fairness, uncertainty quantification, and interpretability, organised around a concept I call discriminatory uncertainty — the idea that a model can discriminate not only through its decisions, but through how confident it is in them.
Peer‑reviewed articles and working papers from the doctoral programme will be listed here as they go up.
A library that maps a tabular model’s raw features into business‑level concepts and visualises how much of each concept the model actually uses. Includes concept‑level ablation (permutation, SHAP‑marginal, retrain), missingness diagnostics, and regulatory tag overlays.
SHAP‑Weighted Impact Feature Testing for ML system monitoring. Weights feature drift by SHAP‑derived impact, so the alerts you get are the alerts that actually move the model’s output.
Industry‑mentored project for the Research Workshop course at the Faculty of Mathematics and Information Science. Three companies contribute a project each year and guide the student team through it; ours is on conformal prediction applied to consumer‑credit scoring. I co‑prepared the brief and mentor the team. Course materials and the starter repository will appear here as the iteration finishes.
More teaching materials — lectures, talks, workshop notes — will appear here as they go up.