Jérémy Lesuffleur is a Machine Learning Engineer with a decade of experience applying statistical rigor and ML to real-world time series and energy problems, currently building models at Meta. His background blends an engineering degree in statistical methods with a research Master's in mathematical statistics, and he has focused much of his career on load forecasting, anomaly detection, and time-series encoding at EDF. He also spent time as an applied scientist in media tech (Jellysmack), showing an ability to transfer forecasting and modeling skills across domains. Known for bridging research and production, he repeatedly moves prototypes into deployed systems that scale in operational settings. Fluent in both French and international research contexts (Erasmus in Sweden), he brings a quantitative mindset comfortable with complex probabilistic models and industrial constraints. Colleagues describe him as a pragmatic scientist who prefers measurable impact over theoretical novelty.
9 years of coding experience
8 years of employment as a software developer
Semestre en Erasmus, Mathématique et Informatique, Semestre en Erasmus, Mathématique et Informatique at Linköpings universitet
Master 2 Recherche, Statistiques mathématiques et probabilités, Master 2 Recherche, Statistiques mathématiques et probabilités at Université Paul Sabatier (Toulouse III)
Diplôme d'ingénieur, Méthodes et Modèles Statistiques, Diplôme d'ingénieur, Méthodes et Modèles Statistiques at Institut national des Sciences appliquées de Toulouse
Contributions:2 pushes, 1 branch in 1 year 7 months
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Jérémy Lesuffleur - Machine Learning Engineer at Meta