About the Principal

Built the stock-selection engine behind Schwab's equity ratings: large-cap and small-cap ratings models covering domestic and international securities, used to rate the universe retail investors actually trade. Led a team of two senior quants building the companion equity risk model, and delivered a separate short-horizon statistical factor model for active traders.

• Large- and small-cap ratings models, domestic + international
• Long-horizon equity risk model (team lead)
• Short-horizon statistical factor model for active traders
• Factor anomaly research, regime analysis, performance attribution

Two decades of the machinery that prices and hedges optionality — from calibrating stochastic-volatility models on a trading desk to owning the consolidated pricing library after one of the largest brokerage mergers in the industry.

• SABR in C++ with Nelder-Mead calibration; enhanced volatility-shock model benchmarked against SVI
• Daily SVI model-performance monitoring in production
• Improved CRR control-variate model benchmarked against Bjerksund-Stensland (Java)
• SABR and Heston calibration for commodity options; GJR-GARCH volatility

Applied deep learning where it earns its keep — forecasting volatility for concentrated positions, the exact place a retail brokerage carries the most tail risk.

• LSTM-GARCH volatility models for concentration portfolios
• GARCH and neural-network sector volatility forecasting
• Time-series, regime, and anomaly research feeding production models

Risk budgeting, attribution, and aggregation across nine asset classes of a general-account portfolio — including the illiquid sleeves most models quietly ignore: real estate, commercial mortgages, and private equity.

• GAAP and economic risk attribution for rate, credit, equity, currency
• Swaption-based hedging keyed to economic and interest-rate regimes
• Yield-curve roll optimization for treasury, credit, and municipal portfolios
• Weekly reporting to the Risk & Investment Committee

Models built for people who lose money the same day they are wrong. Market-making and statistical-arbitrage strategies used by proprietary desks and hedge funds, and portfolio research for a hedge fund's allocation process.

• High-frequency options market-making and stat-arb strategies for Goldman Sachs, Merrill Lynch, Peak6, and Getco
• Index volatility and correlation arbitrage guidance
• BG Capital ($10M hedge fund): factor models, sector rotation, forecasting, optimization, and attribution built in R

The discipline that separates a backtest from a system you can defend: capital adequacy, margin, and stress frameworks reviewed by regulators.

• CCAR / DFAST / Basel capital adequacy, liquidity-gap and stress analysis for Schwab Bank
• SPAN 2 risk-array analysis and unsecured margin debt risk with VIX-shock sensitivity
• Monte Carlo VaR and XVA for concentration exposures
• $60M synthetic CDO rating-migration risk under S&P methodology — negotiated capital charges with S&P and Moody's, realized $1.2M
• Credit models estimating probability of default and loss for Schwab Bank

Everything above now runs as one system I own end to end — research through execution — which is why the process behind a client portfolio is the same process I run with my own capital.

• Signal research, backtesting, portfolio construction, and options overlays
• Position and order management across every asset traded
• Macro dashboard on FRED, Quandl, and Polygon feeds
• Tax-aware tactical process: ETF rotation, pair trades, option hedging
• Portfolio manager for 401(k) and profit-sharing plans, $1M–$5M