Summary
Rushab Munot is an equity quantitative researcher based in the New York City area with nine years of experience building and refining alpha models for asset management. He combines a strong academic foundation—a CS MS from the University of Pennsylvania and a CS bachelor from IIT Kanpur—with hands-on experience across market risk, special situations, and research roles at Goldman Sachs and Lord, Abbett. His background spans ML and NLP research (including work on emotion-aware speech recognition and text summarization) and production quant systems, enabling him to bridge cutting-edge modeling techniques and real-world portfolio constraints. Notably, he has implemented deep learning solutions for hierarchical tasks early in his career and translated academic methods into quant workflows under tight regulatory environments. Colleagues describe him as a pragmatic researcher who rapidly prototypes ideas and drives them to production-ready implementations. He brings a rare mix of research rigor, trading-room pragmatism, and cross-domain engineering experience.
9 years of coding experience
4 years of employment as a software developer
Higher Secondary Education, Science, bifocal with Computer Science, 90.76%, Higher Secondary Education, Science, bifocal with Computer Science, 90.76% at Abasaheb Garware Junior College
Indian Institute of Technology Kanpur
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Pennsylvania
Marathi, Hindi, English