Guillaume Lemaitre
VP Of Product Strategy
Palaiseau, Île-de-France, France
Summary
🤩
Rockstar🎓
Top SchoolGuillaume Lemaitre is a machine learning and open-source engineering leader with 12 years of experience, currently shaping product strategy as VP of Product Strategy at :probabl. and serving as an OSS engineer for scikit-learn. He has deep hands-on expertise in Python ML tooling—contributing core features and robustness improvements to scikit-learn, pandas, joblib and ecosystem projects like imbalanced-learn and OpenML (notably adding Pandas DataFrame support and dataset handling). His background combines academic research in medical imaging and a PhD-era focus on computer-assisted diagnosis with practical CI/CD and documentation improvements across projects such as pydicom and joblib. Guillaume is as comfortable implementing numerical algorithms (Laplace operator, local geometric mean filters) and sampling methods (NearMiss variants) as he is guiding product and engine-level decisions, reflecting a rare blend of researcher rigor and production-grade engineering. Based in Palaiseau, France, he consistently improves tooling interoperability and reproducibility across the ML Python stack.
12 years of coding experience
8 years of employment as a software developer
Bachelor of Science Electronic Signal and Image, Bachelor of Science Electronic Signal and Image at Université Bourgogne EuropeMaster Computer Vision and Robotics, Master Computer Vision and Robotics at Université de Bourgogne, Heriot-Watt University, Universitat de GironaMaster in Science Business Innovation and Technology Management, Master in Science Business Innovation and Technology Management at Universitat de Girona
Stackoverflow
11reputation
2kreached
1answer
1question
Github Skills (61)
data-visualizations10
caching10
algorithms10
github-ci10
python10
evaluation10
image-processing10
data-science10
sphinx10
data-set10
scikit10
testing10
pandas10
machine-learning10
bash10
Programming languages (19)
Github contributions (5)
scikit-learn-contrib/imbalanced-learn
May 2015 - Dec 2022
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Role in this project:
Data Scientist
Data ScientistContributions:48 releases, 68 reviews, 876 commits in 7 years 8 months
Contributions summary:Guillaume's contributions center on implementing and refining machine learning algorithms, specifically those related to imbalanced datasets. Their work focuses on the implementation of the NearMiss algorithms and ensuring that these methods are properly tested, as evidenced by the creation and modification of test files. Furthermore, their work focuses on implementing and refining machine learning algorithms, as demonstrated by the contributions to the implementation of new over-sampling and under-sampling algorithms for data processing and to support a proper evaluation of the data used by the algorithms.
machine-learningpythonstatisticsdata-sciencedata-analysis
pydicom/pydicom
Jul 2017 - Apr 2021
Read, modify and write DICOM files with python code
Role in this project:DevOps Engineer
DevOps EngineerContributions:471 commits, 27 PRs, 443 pushes in 3 years 10 months
Contributions summary:Guillaume primarily focused on improving the continuous integration and continuous deployment (CI/CD) pipeline for the pydicom project. Their work included setting up and configuring CI environments using Travis CI, Appveyor, and CircleCI. They also updated build scripts, managed dependencies, and ensured proper documentation generation within the CI/CD framework. Furthermore, they implemented steps to automatically deploy documentation to GitHub Pages.
dicompythonpydicom
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