Charles Masson
Member Of Technical Staff at Genesis AI
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolCharles Masson is a Member of Technical Staff based in Paris with 11 years of experience building data-driven software and ML systems across startups and fast-growing engineering teams. He progressed through multiple data science and engineering roles at Datadog—culminating as Staff Engineer—bringing deep production experience in observability, scalable services, and data workflows. Charles contributed to core scientific tooling as a data scientist in the widely used SciPy repository, implementing and testing statistical distance functions like Wasserstein and energy distance. He blends research-oriented thinking from recent roles as a Core Team Researcher with hands-on product delivery at companies like Genesis AI. Trained at Mines Paris (PSL) in Information Systems Management, he pairs quantitative rigor with pragmatic engineering. Colleagues would describe him as someone who moves fluidly between algorithmic detail and large-scale system design.
11 years of coding experience
9 years of employment as a software developer
Master degree in Science and Executive Engineering Information Systems Management, Master degree in Science and Executive Engineering Information Systems Management at Mines Paris - PSL
English, Japanese, Spanish, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
statistics10
algorithms10
scipy10
python10
statistic10
numpy9
testing9
Programming languages (9)
Github contributions (5)
scipy/scipy
Jul 2017 - Aug 2017
SciPy library main repository
Role in this project:
Data Scientist
Data ScientistContributions:13 commits, 1 PR, 9 comments in 1 month
Contributions summary:Charles primarily contributed to the `scipy/scipy` repository by implementing and testing statistical distance functions, specifically Wasserstein and energy distance. Their work involved adding new functions to the `scipy.stats` module, including the core logic for calculating these distances and writing comprehensive tests to ensure their correctness and robustness. The user also refactored and improved existing code, focusing on efficiency and best practices within the codebase.
scipypythonscientific-computing
DataDog/sketches-java
May 2019 - Dec 2021
DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees.
Contributions:10 releases, 51 reviews, 184 commits in 2 years 6 months
sketch
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