Binny Paul is a Principal Data Scientist and transportation engineer with 10 years of experience translating complex mobility data into policy-ready models and forecasts. He has deep expertise in travel demand and behavioral modeling, transport network analysis, discrete-event simulation, and applied machine learning—skills honed across consulting and industry roles including RSG, PTV Group, and SH 130 Concession Company. Binny blends rigorous econometric and statistical methods with practical ETL and data-visualization workflows (R, Python, SQL) to deliver transit ridership forecasts, emerging-mode analysis, and optimization-driven solutions. His academic work on PHEV impacts and emissions policy adds a rare research-to-practice perspective that informs decisions on carbon and infrastructure investments. Based in Austin, he pairs technical leadership with hands-on model development to help agencies make evidence-based, operational decisions.
10 years of coding experience
7 years of employment as a software developer
Indian Institute of Technology Madras
MS Transportation Engineering, MS Transportation Engineering at The University of Texas at Austin
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