Louis Lacombe
Data Scientist
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolLouis Lacombe is a Paris-based Data Scientist with five years of experience applying rigorous statistical methods and machine learning to real-world problems, currently contributing at Quantmetry. He specializes in uncertainty quantification and conformal prediction, evidenced by active contributions to the scikit-learn-contrib MAPIE project to improve prediction-interval and CQR functionality. Trained at Bocconi and Erasmus with exchange experience at Mannheim and LSE, he blends strong econometrics foundations with applied data science practice. Louis has moved smoothly between industry internships and production roles (Stellantis, Danone) and brings a practical mindset from early tutoring and operations work that helps translate complex models into usable business insights. Notably, his open-source work focuses on testing and implementing core library capabilities, reflecting a developer-oriented approach to trustworthy ML.
5 years of coding experience
1 year of employment as a software developer
Data Science and Business Analytics, Data Science and Business Analytics at Università BocconiKoninklijk Conservatorium - Royal ConservatoireHigh School, High School at International School of The HagueBachelor Exchange Program, Bachelor Exchange Program at University of MannheimInternational Bachelor Econometrics and Operations Research, International Bachelor Econometrics and Operations Research at Erasmus School of EconomicsLondon School of Economics and Political Science
French, English, Dutch, Spanish, German, Italian
Github Skills (5)
scikit10
testing10
machine-learning10
python10
scikit-learn10
Programming languages (6)
Github contributions (5)
scikit-learn-contrib/MAPIE
May 2022 - Jan 2023
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
Role in this project:
Data Scientist
Data ScientistContributions:5 releases, 620 reviews, 195 commits in 8 months
Contributions summary:Louis appears to be contributing to a machine learning library designed for estimating prediction intervals using conformal prediction techniques. Their initial commit installs a library for Conformalized Quantile Regression (CQR), and further commits involve adding or modifying code related to quantile regression and testing, specifically within the context of the "mapie/mapie" library for prediction interval estimation. The user's focus is on testing and implementing functionalities related to CQR, which indicates contributions to improving the core capabilities of the prediction intervals.
regressionpythonconfidence-intervalspredictiondata-science
LacombeLouis/MAPIE
Nov 2022 - Mar 2024
A scikit-learn-compatible module for estimating prediction intervals.
Contributions:5 pushes in 1 year 4 months
pythonpredictiondata-scienceestimatingmachine-learning
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