Sylvain Lesage is a seasoned datavisualization and software developer with 14 years of multidisciplinary experience spanning data science, geospatial systems, and web-scale dataset tooling. Based in Rennes, France, he builds production-grade data viewers and back-end services—most notably contributing to the Hugging Face Datasets and Evaluate libraries and to the OWSLib geospatial stack. He combines strong engineering (Python, Node.js, MongoDB, Kubernetes) with interactive front-end craft (D3.js, Svelte, Observable) to turn complex datasets into usable, beautiful interfaces. His career uniquely mixes government-level infrastructure and standards work in Bolivia with research and academic teaching, giving him rare expertise in standards, PKI, and spatial data infrastructures. A meticulous technical writer as well as coder, he frequently improves documentation and developer ergonomics alongside core features. He also runs a dataviz freelance practice and continues part-time at Hugging Face, blending open-source impact with client-facing product work.
13 years of coding experience
13 years of employment as a software developer
Engineer Signal processing, Engineer Signal processing at CentraleSupélec
Doctor of Philosophy - PhD Telecommunications and signal processing, Doctor of Philosophy - PhD Telecommunications and signal processing at Université de Rennes I
Master Applied Mathematics, Master Applied Mathematics at Université de Metz
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Role in this project:
Back-end Developer & Technical Writer
Contributions:46 reviews, 13 commits, 35 PRs in 1 year 6 months
Contributions summary:Sylvain primarily focused on improving the functionality and documentation of the Hugging Face Datasets library. Their contributions included fixing bugs related to string formatting and URL handling within the streaming download manager, as well as adding new features like retrieving dataset split names. The user also significantly contributed to the documentation, improving clarity, fixing typos, and adding examples. These changes demonstrate expertise in the library's internal workings and a commitment to enhancing user experience.
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
Role in this project:
Back-end Developer
Contributions:6 commits in 7 months
Contributions summary:Sylvain primarily contributed to the documentation and internal functions of the evaluate library. They fixed string formatting issues to improve usability, and added/modified documentation details, including links and code examples. A significant portion of the commits involved adding a function to get dataset config's split names and passing tokens for API access. The user demonstrated a focus on the dataset inspection and configuration aspects of the library.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.