Xinrui (Eunice) Yang is an M.S. in Computational Finance student at Carnegie Mellon University with a strong foundation in statistics and finance and experience applying quantitative models to complex real-world problems. Her coursework includes Financial Data Science, Machine Learning, Financial Time Series, Risk Management, Options, and Fixed Income, building on her undergraduate degrees in Statistics and Business Administration with a Finance concentration. Eunice has developed quantitative experience across options and portfolio risk management. At TradingFlow, she built and backtested a GARCH-based options risk-screening pipeline, calibrating volatility and market-activity signals to improve risk filtering and increase the detection of elevated-volatility contracts by approximately 20%. At Eldon Group, she applied PCA, stress testing, and multifactor modeling to analyze portfolio risk exposures, reducing the Treasury yield curve to three factors explaining 98% of its variation and developing an expanded factor model that explained 91.8% of portfolio-return variation. These experiences strengthened her ability to build, validate, and interpret quantitative models while translating their results into practical risk and investment insights. Eunice has also worked on quantitative problems in the insurance industry. At Group 1001, a residual value insurance company, she developed a weighted least squares residual-value model using 15 years of vehicle auction data and built a reusable model-validation framework for missingness, outliers, multicollinearity, and heteroskedasticity. Through this experience, she gained exposure to how quantitative valuation and risk models support insurance underwriting and business decisions. Her project work further extends this skill set to healthcare, where she developed machine-learning models for heart-failure readmission risk and applied VaR, CVaR, and scenario analysis to quantify potential financial losses. Eunice is particularly interested in risk quant, portfolio quant, quantitative research, and insurance quant roles where she can combine statistical modeling with financial judgment, uncover patterns in noisy real-world data, and translate quantitative insights into practical business decisions.
