Served as the Quantitative Risk Manager, collaborating with the Equity Desk to provide real-time analytical support for counterparty risk measurement and management of equity derivatives and structured products.• Researched and enhanced the dynamic initial margin model, which had been underestimating risk based on the Standard Initial Margin Model (SIMM). Addressed limitations in simulation time horizons for risk management within the potential future exposure model (a Monte Carlo framework).• Analyzed key risk exposures from corporate derivatives, including accelerated share repurchase agreements, registered equity forwards, corporate call spreads, and hedging and monetization products such as variable prepaid forwards.• Developed a benchmark model to test and validate the Desk’s house-margin model (historical simulation model) for hedge fund clients in equity derivatives, resulting in more rigorous margin levels and a robust risk management process.

Successfully led model testing and validation projects generating over ten million dollars in revenue with multiple top US banks, focusing on FX, CVA, Counterparty Risk Exposure, and Wealth Management models.• Designed and implemented diagnostic and testing tools for FX front office valuation models and Counterparty Credit Risk (CCR) Exposure models at one of the largest US banks. Led a team of over ten professionals across three years, conducting in-depth analyses of FX models (including FX stochastic volatility pricing and barrier option pin risk) and CCR/Credit Valuation Adjustment (CVA) models (such as Longstaff-Schwarz Method, Carr Randomization, and multi-tier commodity curve simulations). Personally developed Python programs to rigorously test and document these models in compliance with Supervisory Letter 11-7 standards.• Led a team to support the development and testing of a goals-based wealth management model in Matlab for a leading US investment bank. This included capital market assumptions, market factor Monte Carlo simulations, strategy selection and calculating goal-reaching probabilities. Proposed and implemented a full suite of model development tests in accordance with Fed Supervisory Letter 11-7 requirements, including implementation testing, sensitivity analysis, benchmarking, stress testing, and expert judgment evaluation.

Led the bank’s initiative to enhance counterparty risk internal models in compliance with Basel III requirements and conducted backtesting of Monte Carlo simulation models.• Improved the bank’s counterparty risk exposure model backtesting using MATLAB by introducing full-distribution-based testing, addressing modeling effectiveness for expected exposure, and implementing bootstrap methods for long-term simulations, considering data availability constraints.• Designed, implemented, and tested the firm’s Internal Model Method (IMM) for Basel III, including models for wrong-way risk trades, collateral, margin requirements, and stress parameter calibration. Conducted extensive quantitative modeling to ensure compliance and effectiveness.

Led model testing and validation projects with multiple U.S. banks across Market Risk, Operational Risk, Credit Risk, FX, and Counterparty Risk Exposure models.• Served as the lead statistician and modeler for the New York team on the Operational Risk Advanced Measurement Approach (AMA). Developed an R analytical library for end-to-end AMA economic capital calculations, including fat-tail distribution fitting (Pareto, lognormal, Student’s t-distribution, Weibull, Burr), Anderson-Darling, and Kolmogorov-Smirnov goodness-of-fit tests, as well as Monte Carlo simulations using copula.• Developed benchmark models for FX rate simulation and the valuation of exotic FX products. Utilized Black-Scholes, local volatility, and stochastic volatility frameworks, applying explicit finite difference methods to for present value calculations of FX barrier trades.• Constructed benchmark logistic regression models in R for predicting the probability of default (PD). Leveraged SNL financial ratios as predictors, combined with five years of historical default records, and enhanced the accuracy of PD projections and modeling effectiveness.

Dedicated to supporting traders in modeling and calculating pre-trade counterparty risk exposure. Enhanced and tested the bank’s model library for exposure assessment.