Summary
Sriram Nagaraj is a Senior Data Scientist with seven years of experience translating advanced mathematical theory into resilient, auditable, production-ready models for regulatory stress testing, credit risk, and risk analytics at Federal Reserve Banks. His background spans applied R&D in physics-informed ML, probabilistic learning, and signal processing for aerospace at Southwest Research Institute, plus leadership of AI efforts at a fintech startup in the payments/Blockchain space. He pairs deep theoretical training (PhD-level computational science) with hands-on deployment of ML/NLP models used in supervision, regulatory examinations, and model risk management across the Federal Reserve System. Known for bridging rigorous numerical methods and practical ML engineering, he co-led peer-reviewed code dissemination efforts and taught internal courses—an unusual blend of research, mentorship, and production discipline. Based in Greater Cleveland, he brings a track record of moving novel ML research into auditable, high-performance systems for high-stakes financial and scientific settings.
7 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Electrical Engineering, Bachelor's degree, Electrical Engineering at The University of Texas at Dallas
Doctor of Philosophy - PhD, Computational Science, Engineering and Mathematics (CSEM), Doctor of Philosophy - PhD, Computational Science, Engineering and Mathematics (CSEM) at The University of Texas at Austin
Master of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at Rice University