Benjamin Donnot is a data scientist with 11 years of experience specializing in applying deep learning and reinforcement learning to real-world power grid operations. He completed an industry-embedded PhD at RTE/Paris-Saclay focused on machine learning methods to improve reliability and efficiency amid rising intermittent renewables, and continues to deploy research into production at France’s transmission system operator. His background spans forecasting, quantitative modeling and applied research across finance, aviation and utilities, with hands-on experience in Python, R and C++ and teaching data science at ENSEA. Notably, he leverages operator historical actions to build practical, human-informed ML solutions that bridge domain expertise and automated decision-making.
11 years of coding experience
3 years of employment as a software developer
Ph. D, Ph. D at Paris Saclay University
Master's Degree, Applied Mathematics, Diplôme obtenu ave mention A, Master's Degree, Applied Mathematics, Diplôme obtenu ave mention A at Ensae ParisTech
Contributions:3 releases, 61 reviews, 9 commits in 2 years 8 months
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