Louis Lacombe
Data Scientist
Paris, Ile-de-France
Summary
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RockstarLouis Lacombe is a Paris-based Data Scientist with four years of experience at Quantmetry, progressing from intern to full Data Scientist. He focuses on applied machine learning and uncertainty quantification, contributing to the scikit-learn-contrib MAPIE project where he implemented and tested conformalized quantile regression features for robust prediction intervals. Louis holds an MSc in Data Science and Business Analytics from Bocconi and brings a strong quantitative foundation from studies across Erasmus, Mannheim and LSE. He combines production-facing experience at firms like Stellantis and Danone with a knack for clear technical communication—evidenced by prior mathematics tutoring. An unconventional detail: earlier training at the Royal Conservatoire suggests a creative, multidisciplinary approach to problem solving.
4 years of coding experience
Github Skills (5)
scikit-learn10
testing10
machine-learning10
python10
scikit10
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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