Vincent Maladière
Applied Scientist at Mistral AI
Paris, Ile-de-France, Singapore
Summary
🤩
Rockstar🎓
Top SchoolVincent Maladière is an applied scientist with 10 years of experience building ML systems and research-grade tooling, currently at Mistral AI after a research stint at Inria. He co-founded Probabl.ai, helped raise a €5.6M seed round, and has deep product instincts informed by 50+ user interviews with data scientists on MLOps needs. His open-source contributions to scikit-learn, skrub and hazardous include both algorithmic speedups—up to 50x for sparse pairwise distances—and rigorous documentation and validation work that improves usability for the wider ML community. He co-authored a state-of-the-art survival analysis paper that blends gradient-boosted trees with proper scoring rules and regularly presents applied survival work at conferences. Comfortable shipping production systems (FastAPI, Docker, LightGBM) and prototyping research code, he bridges academia and industry with experience at unicorns and startups. Based in Paris with a background from Grenoble INP, he brings a pragmatic, experiment-driven approach to tabular, survival, and causal ML problems.
10 years of coding experience
6 years of employment as a software developer
Master of Science in Engineering, Signal Communication and Multimedia, Master of Science in Engineering, Signal Communication and Multimedia at Grenoble INP - PhelmaMathematics and Physics, Mathematics and Physics at Lycée Pierre De Fermat
French, English, Spanish
Stackoverflow
11reputation
1kreached
1answer
0questions
Github Skills (14)
scikit-learn10
scikit10
machine-learning10
numpydoc10
python10
data-science10
sphinx10
data-analysis10
documentation9
statistics9
validations8
validation8
ipywidgets6
jupyter6
Programming languages (7)
Github contributions (5)
skrub-data/skrub
Sep 2022 - Nov 2022
Prepping tables for machine learning
Role in this project:
Data Scientist
Data ScientistContributions:452 reviews, 6 commits, 111 PRs in 2 months
Contributions summary:Vincent's contributions primarily focused on improving the project's documentation and ensuring proper code formatting using numpydoc. They replaced the documentation generator with numpydoc and corrected a typo in the documentation configuration. Moreover, the user added docstring tests to ensure that the docstrings of class methods are formatted according to numpydoc specifications. These changes enhance the maintainability and readability of the codebase, aligning with data science best practices.
autoencoderdata-preprocessingdatadata-sciencetabular-data
scikit-learn/scikit-learn
Aug 2022 - Jan 2023
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:86 reviews, 20 commits, 21 PRs in 5 months
Contributions summary:Vincent primarily contributed to improving the documentation and validation of various machine-learning metrics within the scikit-learn library. Their work involved ensuring that docstrings for functions like `mutual_info_score`, `normalized_mutual_info_score`, `pair_confusion_matrix`, `rand_score`, and `v_measure_score` passed numpydoc validation. Additionally, the user added parameter validation for certain estimators, such as `ShrunkCovariance` and `SkewedChi2Sampler`. These changes demonstrate a focus on enhancing the quality, usability, and maintainability of the machine-learning library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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