Summary
Rajeev Godse is a quantitative trader and Carnegie Mellon-trained computer scientist with eight years of experience bridging algorithmic research, ML, and production software. Currently at Jane Street, he has progressed from intern to trader, previously extending OCaml compilers and building a query language for temporal logic while also delivering high-impact ML infrastructure at Meta. His background includes satellite and hyperspectral imaging projects that fused domain knowledge with machine learning to produce real-time onboard processing and novel multi-resolution search techniques. A seasoned teaching assistant and curriculum developer, he brings clear technical communication and pedagogy to complex systems. Known for combining rigorous mathematical thinking with practical implementation, he thrives on turning theoretical ideas into low-latency, real-world trading and ML systems.
9 years of coding experience
2 years of employment as a software developer
High School Diploma, High School Diploma at Fox Chapel Area High School
Management & Technology Summer Institute at UPenn, Management & Technology Summer Institute at UPenn at Jerome Fisher M&T Program
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Carnegie Mellon University