• Provided a range of valuation advisory services to private equity and hedge fund clients in complex assets positions, e.g. asset-backed securities (ABS) and peer to peer lending• Designed financial models for clients’ portfolios; created waterfall models for marketplace loan securitizations• Valued various loan portfolios including an auto loan securitization CDS position• Supervised the onboarding of new members to the team and managed team running daily and monthly loan… Show more • Provided a range of valuation advisory services to private equity and hedge fund clients in complex assets positions, e.g. asset-backed securities (ABS) and peer to peer lending• Designed financial models for clients’ portfolios; created waterfall models for marketplace loan securitizations• Valued various loan portfolios including an auto loan securitization CDS position• Supervised the onboarding of new members to the team and managed team running daily and monthly loan valuation system Show less

• Analyzed financial statements and created cash flow projection model in excel to evaluate target companies • Took the lead in predicting a biotechnology firm’s future revenue by analyzing potential growth on the company’s current drug portfolio, including a Phase III drug candidate

• Managed a long/short portfolio with book fully closed by end of day; strategies were designed to be low drawdown, high sharpe: had sharpe ratio of 8 with annualized return of 10%• Implemented Bayesian market maker algorithm from academic papers in Python; tested for most efficient uninformed trader percentage and spread size model coefficients using historical tick data for a single stock• Tested possibility of using machine learning (a recursive neural net) to predict future stock… Show more • Managed a long/short portfolio with book fully closed by end of day; strategies were designed to be low drawdown, high sharpe: had sharpe ratio of 8 with annualized return of 10%• Implemented Bayesian market maker algorithm from academic papers in Python; tested for most efficient uninformed trader percentage and spread size model coefficients using historical tick data for a single stock• Tested possibility of using machine learning (a recursive neural net) to predict future stock returns based on value-style data inputs (P/E, P/B, ROE, etc) Show less

Taught two English classes with class size of 15 studentsSupervised and assisted students with essay writing for applications to English-speaking graduate schools

Applied statistical methods for analyzing sports games and teamsCreated betting algorithms using python scripting to beat the spread and provide average returns of over 200% for 4 years. Created small account to test algorithm and had positive returns all 4 years

Evaluated gene association data in R to find genes that promote positive fetal outcomesParticipated in daily training sessions to enhance knowledge of the reproductive cycle, genomics, and statistical analysis