Prashant Rai is a Senior Staff Data Scientist based in San Francisco with nine years of industry experience and a PhD in applied mathematics focused on statistical learning. He builds production-ready ML solutions across aerospace, energy, retail, and heavy machinery domains, with strengths in multivariate time series, anomaly detection, and risk quantification. Prashant’s research expertise in low-rank tensor decompositions drives practical methods for high-dimensional regression and stochastic-function approximation that he translated from academia at Ecole Centrale Nantes and Sandia National Labs into patented industrial applications at Caterpillar. At Baker Hughes and Albertsons he has scaled analytics for operational decision-making, and he has led teams that bridge scientific rigor with deployable engineering. Colleagues describe him as a researcher-engineer who turns advanced multilinear algebra into robust, interpretable models for real-world IoT and forecasting problems.
9 years of coding experience
15 years of employment as a software developer
BE, Mechanical Engineering, First class with distinction, BE, Mechanical Engineering, First class with distinction at University of Mumbai
Master’s Degree, Computational (Applied) Mechanics, Master’s Degree, Computational (Applied) Mechanics at Ecole Centrale Nantes
Contributions:54 pushes, 1 branch in 1 year 10 months
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