Summary
Abhinuv Uppal is a Full Stack Engineer with 8 years of experience blending software development, data engineering, and applied analytics across public sector and startup environments. He holds an MS in Analytics from Georgia Tech and graduated summa cum laude in Physics and Applied Mathematics with a Data Science focus from Claremont McKenna, where he also produced original research on Bayesian learning. At the Federal Reserve Board he translated macroeconomic research needs into data-driven tooling for international finance, and now builds end-to-end systems at Strider Technologies. Comfortable moving between research, production code, and team leadership, he has led analytics initiatives for small businesses and taught data science workshops while contributing to machine learning projects. Colleagues rely on him for turning complex economic problems into reliable, testable software and repeatable analytics workflows.
8 years of coding experience
4 years of employment as a software developer
Physics and Applied Mathematics, Data Science, Physics and Applied Mathematics, Data Science at Claremont McKenna College
Master of Science - MS, Analytics, Master of Science - MS, Analytics at Georgia Institute of Technology