Summary
Ulrich Goue is a Senior Data Scientist based in Paris with eight years of experience applying advanced quantitative methods to finance, insurance, healthcare, and public policy. Trained at ENSAE and ENS Paris-Saclay (MVA), he blends deep mathematical rigor with practical ML deployment—optimizing real-time clinical models, credit-rating early-warning systems, and customer churn/segmentation pipelines. He has moved between research and product settings, from The World Bank and academic teaching to industry roles at Crédit Agricole, Capgemini Invent, and KLESIA, consistently translating domain knowledge into informative features and robust models. Passionate about macroeconomic modeling and social-science applications of AI, he pairs strong writing and teamwork skills with the ability to lead projects autonomously under pressure. Notably, he combines econometric intuition with modern techniques (NLP, graph methods, deep learning) to make complex models interpretable and operational.
7 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng Quantitative Economics & Statistics, Master of Engineering - MEng Quantitative Economics & Statistics at Ecole Nationale Supérieure de Statistique et d'Economie Appliquée ENSEA
A' level Mathematics and Physics, A' level Mathematics and Physics at Technical and Preparatory Military Academy of Bingerville
Master of Science - MS Mathematics & Computer Vision & Machine Learning (MVA), Master of Science - MS Mathematics & Computer Vision & Machine Learning (MVA) at École Normale Supérieure Paris-Saclay
Bsc in Mathematics and Physics, Bsc in Mathematics and Physics at Institut National Polytechnique Félix HOUPHOUËT-BOIGNY de Yamoussoukro (INP-HB) Officiel
Master of Engineering - MEng Applied Mathematics & Data Science (Statistical Learning), Master of Engineering - MEng Applied Mathematics & Data Science (Statistical Learning) at ENSAE Paris
French, English, German