At RBC Capital Markets, I collaborated with traders on an automated hedging solution that reduced hedging PnL variance by 23% on a $150M book. I also developed C++ pricing modules integrated into the front-office platform, accelerating deal pricing from 120 ms to 45 ms.
At BMO Capital Markets, I automated three QIS strategies with Python and KDB, handling 847 client portfolios daily. I also produced real-time risk dashboards for tracking PnL across 1,200+ instruments.
At Ford Credit, I built credit risk scoring models that reduced default prediction error by 18% on a 50K-loan dataset. I designed automated data ingestion workflows that shortened data preparation from five hours to 45 minutes.
Earlier, at Capital One, I used statistical and machine learning models to optimize marketing spend, raising campaign ROI by 28%. At TD Securities, I developed C++ equity option pricers and a Python Greeks calculation engine for trader terminals.
