Jakub Wiśniewski
Consultant at Deloitte
Copenhagen, Capital Region of Denmark
Summary
🤩
Rockstar🎓
Top SchoolJakub Wiśniewski is a consultant and data scientist with seven years of experience building ML-driven tools for finance and fairness-aware AI, currently working at Deloitte after roles at SEB and MI2DataLab. He combines practical production work—containerized Python apps, Neo4j network exploration, and generative-AI reporting—with research-grade contributions to open-source fairness tooling (notably the DALEX fairness module and the fairmodels R package). Trained in Human-Centered AI at DTU and with a Data Science engineering background from Warsaw University of Technology, he bridges technical rigor and usability in model explanation and bias detection. Jakub’s work has helped treasury teams visualize liquidity and payment inefficiencies while also enabling radiology annotation workflows, reflecting a knack for turning domain complexity into actionable tools.
7 years of coding experience
5 years of employment as a software developer
Inżynier (Inż.), Inżynieria i Analiza Danych (Data Science), Inżynier (Inż.), Inżynieria i Analiza Danych (Data Science) at Warsaw University of TechnologyTechnical University of Denmark
Github Skills (13)
scikit-learn10
machine-learning10
explainable-artificial-intelligence10
interpretation10
f10
python10
n10
scikit10
testing9
plotly9
unit-test9
pandas9
unit-testing9
Programming languages (8)
Github contributions (5)
ModelOriented/DALEX
Oct 2020 - Jul 2021
moDel Agnostic Language for Exploration and eXplanation
Role in this project:
Data Scientist
Data ScientistContributions:9 reviews, 16 commits, 13 PRs in 8 months
Contributions summary:Jakub primarily contributed to the "fairness" module, heavily involved in implementing and testing fairness metrics and plots. Their work includes adding new plots for fairness visualization, fixing existing plotting issues, and expanding support for regression models within the fairness framework. They also focused on integrating various fairness-related methods and tests within the dalex library, ensuring the robustness and accuracy of the fairness functionalities. They made several improvements to the documentation.
xaishapagnosticblack-boxexplainable-ml
Bozhi-Lyu/MLOpsProj
Jan 2024 - Jan 2024
Contributions:4 reviews, 36 PRs, 67 pushes in 12 days
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