Summary
Lalit Ponnala is a Director of Global R&D in New York with 11+ years building systematic, multi-asset quantitative strategies and risk analytics across equities, options, commodities and ETFs. He combines a PhD- and postdoc-level research background in computational science with strong Python engineering to lead teams that turn machine learning and time-series research into production-ready portfolio construction and surveillance tools. His experience spans market-making risk systems, surveillance models for manipulative trading, and smart-beta product design, reflecting deep domain knowledge from both buy-side and regulatory perspectives. An author of 40+ peer-reviewed publications, he brings a rigorous, data-driven approach to model validation and back-testing and often bridges high-performance research code with practical data platforms like Spark, Presto and FactSet. Notably, his career threads academic genomics computing to modern quantitative finance, indicating a rare blend of scientific computing and market microstructure expertise.
11 years of coding experience
8 years of employment as a software developer
PhD Electrical Engineering (Major) Statistics (Minor), PhD Electrical Engineering (Major) Statistics (Minor) at North Carolina State University
Bachelor of Engineering - BE Electronics and Communication Engineering, Bachelor of Engineering - BE Electronics and Communication Engineering at National Institute of Technology Karnataka
English