Experience
Quantitative Developer Intern
I worked on machine learning and market analysis algorithms for the Boosted.ai investment platform, including factor models, portfolio optimization, and AI commentary.
Rewrote the core factor model algorithm used by more than 5,000 customer models, reducing scheduled inference time by over 90% and saving more than 500 hours of AWS EC2 computation each week.
Used NumPy, ClickHouse, and PostgreSQL to compute and update daily economic factors for more than 20,000 publicly listed securities and ETFs.
Developed investment style matching for more than 150 institutional clients using Python, gRPC, and Protobuf. The feature analyzes portfolios and reports how well they align with each client’s selected investment style.
Added GraphQL features to the trading algorithm to improve daily stock selection and construct portfolios that better match client needs.
Developed LLM-based commentary for more than 150 institutional clients, generating textual analysis of their portfolios in the context of macroeconomic themes.
