Summary
Matthieu Doutreligne is a statistician and data scientist with 10 years of experience applying machine learning and causal inference to health and public-sector data. He combines academic depth—a PhD on time-varying routine care data and teaching a Machine Learning for Econometrics course at ENSAE—with operational impact as Responsable d’étude at INSEE and prior data science roles at the Haute Autorité de Santé and the French Ministry of Health. His work spans large-scale health data engineering, quality-of-care measurement, and causal analysis of EHRs, with publications and collaborations at MIT and practical tooling published on GitHub (e.g., SNDS-related projects). Comfortable bridging research and production, he has deployed systems in hospital settings and national crisis dashboards, and he brings hands-on experience with representation learning for medico-administrative databases. Based in Paris, he pairs rigorous mathematical training from École Polytechnique and ENSAE with a habit of shipping reproducible research and open-source resources.
10 years of coding experience
3 years of employment as a software developer
Master's degree, Applied Mathematics, Master's degree, Applied Mathematics at Ecole polytechnique
Master 2 (M2), Mathematical Statistics and Probability, Master 2 (M2), Mathematical Statistics and Probability at Ensae ParisTech
Master 2 (M2), Artificial Intelligence, Master 2 (M2), Artificial Intelligence at École Normale Supérieure Paris-Saclay
French, English, German