I am a data scientist and quantitative researcher specializing in systematic trading, financial analytics, and alternative data. I hold an M.S. in Financial Mathematics with a Data Science concentration from the University of Chicago.
In my current role at Boerboel Trading, I build risk decomposition systems, conduct systematic backtests, and develop revenue-nowcasting models for equity research. My work combines market data, credit-card transaction data, Python pricing models, and trading workflows.
I have experience developing quantitative investment strategies and machine-learning models across equities and fixed income. I have worked with alternative datasets, modeled transaction costs and risk-adjusted returns, and built models for illiquid corporate-bond pricing.
My technical background includes Python, SQL, R, C++, Excel/VBA, Bloomberg Terminal, Git, and machine-learning libraries such as NumPy, Polars, scikit-learn, and Matplotlib. I am particularly interested in quantitative research, portfolio risk, asset pricing, and financial modeling.
Before moving into quantitative finance, I worked in healthcare strategy at EY Parthenon, where I performed financial modeling, competitive benchmarking, and market research. This experience strengthened my ability to translate analytical findings into commercial and strategic decisions.
I am also an independent equity researcher with experience creating long theses, DCF valuations, peer-comparison analyses, scenario models, and investment-risk assessments. I enjoy applying rigorous data-driven analysis to investment and trading problems.
