Felix Patzelt is a Senior Data Scientist with eight years of experience applying machine learning, optimization, and quantitative research to mobility and energy challenges, currently driving smart charging optimization at Rabot Energy. Trained as a physicist with a doctorate and research stints in neuroscience and quantitative finance, he blends rigorous modeling, production-grade ML deployment, and scientific curiosity to improve user experience, resource efficiency, and profitability. At MOIA he led demand and supply modelling, built interpretable deep-learning forecasts, and shipped self-service tools that reduced planning overhead while also supporting policy and legal efforts with data-driven evidence. Felix is comfortable across the stack—from AWS operations and data pipelines to optimisation engines—and he often looks beyond algorithms to the surrounding processes and incentives that determine real-world impact.
8 years of coding experience
7 years of employment as a software developer
Doktor der Naturwissenschaften (Dr. rer. nat.) Physics, Doktor der Naturwissenschaften (Dr. rer. nat.) Physics at University of Bremen
Python package for smart binning of one-dimensional data and calculating conditional expectation values.
Contributions:3 PRs, 7 pushes, 4 branches in 4 years 6 months
pythonconditionaldimensionalbinningexpectation
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