Summary
Chinmay Rane is a Senior Quantitative Researcher in NYC with 11 years of experience building production-grade machine learning models and quantitative strategies for macro and fixed-income trading at a top hedge fund. He specializes in end-to-end signal generation, portfolio construction and optimization, and real-time data platforms across futures, swap and bond relative value, and inflation-linked products. Chinmay combines a strong software engineering background (C++, distributed systems) with advanced time-series modeling and statistical optimization from his Georgia Tech QCF MS, enabling models that drive live algorithmic trading P&L. He has a track record of turning research prototypes into scalable, low-latency production systems and often bridges research and engineering teams. Outside core quant work he pursues ML recommendation systems and AI, reflecting a curiosity for applying modern ML patterns to financial markets.
11 years of coding experience
7 years of employment as a software developer
Secondary School Certificate, Mathematics and Computer Science, Secondary School Certificate, Mathematics and Computer Science at IES's Chandrakant Patkar Vidyalaya
Master of Science - MS, Quantitative and Computational Finance, Master of Science - MS, Quantitative and Computational Finance at Georgia Institute of Technology
High School, Computer Science, High School, Computer Science at Ramnivas Ruia Junior College
Bachelor of Engineering - BE, Information Technology, Bachelor of Engineering - BE, Information Technology at Thadomal Shahani Engineering College