Mateusz Krzyziński
Senior Data Scientist
Warsaw, Masovian Voivodeship, Poland
Summary
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Senior🎓
Top SchoolMateusz Krzyziński is a Senior Data Scientist with six years of experience at the nexus of machine learning and security, currently driving cyber analytics and AI innovation at Visa after a recent role hardening ML security at Snowflake. He combines academic rigor—summa cum laude MSc/BSc from Warsaw University of Technology and a research track record with 7+ publications—with hands-on engineering, producing open-source tools like survshap and contributions to dalex and ARTEMIS. His research on Explainable and Trustworthy AI translated into practical packages for explaining survival models and extracting feature interactions, bridging interpretable ML and production needs. He has taught multiple university ML courses and led end-to-end ML deployments for enterprise clients, demonstrating an ability to communicate technical concepts to diverse stakeholders. Comfortable moving between research, teaching, and production, he focuses on making AI auditable and secure in data-rich enterprise environments. An uncommon strength is his expertise in XAI for survival analysis—a niche that informs both his tooling and security-focused work.
6 years of coding experience
4 years of employment as a software developer
Master of Science - MSc, Data Science, 5* (Summa Cum Laude), Master of Science - MSc, Data Science, 5* (Summa Cum Laude) at Warsaw University of TechnologyMathematics, Computer Science, Geography and Physics, Mathematics, Computer Science, Geography and Physics at I Liceum Ogólnokształcące im. Bolesława Prusa w Siedlcach
English, Polish, French
Github Skills (49)
xai10
cox-regression10
r-package10
survival-analysis10
dalex10
interpretable-machine-learning10
iml10
explainable-artificial-intelligence10
meta-analysis10
predictive-modeling10
cross-validation10
biostatistics10
survival9
r9
dtw9
Programming languages (8)
Github contributions (5)
pyartemis/artemis
Nov 2022 - Feb 2023
A Python package with explanation methods for extraction of feature interactions from predictive models
Contributions:148 commits, 3 PRs, 11 pushes in 2 months
ml-modelsxaiderivingmachine-learninginteractions
MI2DataLab/survshap
Aug 2022 - Jan 2023
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Contributions:2 reviews, 37 commits, 22 PRs in 4 months
xaishapvariable-importancebrier-scoresexplainable-ml
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