United States
I’m an analytics professional with a strong quantitative background, currently focused on data and product analytics roles where analysis directly informs decisions. My experience spans data-intensive environments, where I’ve built analytical tools, automated workflows, and worked with messy, high-stakes data. I’m comfortable owning problems end-to-end, from data extraction and quality checks to modeling, insight generation, and communicating results to non-technical stakeholders. I hold an M.S. in Operations Research, and I’ve continued to deepen my technical foundation through hands-on work in machine learning, Python, SQL, and experimentation-style analysis.
Professional Development: Completed M.S. in Business Analytics (GPA: 4.0) and Machine Learning Specialization (Coursera), building advanced skills in data analysis, statistical modeling, and applied machine learning. Personal Development: Focused on longer-term personal goals, including travel (Hawaii, Korea, Iceland) and training for and completing the NYC Marathon, while also exploring personal interests, including improv comedy and volunteering with Second Chance Rescue NYC (animal shelter).
Led the automation of equity derivatives pricing workflows by building Excel- and Python-based tools that integrated internal APIs and pricing libraries, cutting manual trader work by up to 90% and eliminating input errors. Developed Python-based calibration tools to support volatility modeling.
Led development of front-office pricing and risk tools for asset-backed securities, including an expected-loss prediction model that saved over $100,000 per deal. Contributed production-ready pricing features and supported Excel-based tools used to price commercial real estate loans.
SIMM, Risk Not in SIMM Contributed to enhancements of CME’s risk management tooling by analyzing market and correlation data to surface key stress-scenario drivers, preparing price time-series data in SQL and Python, and working with QA to resolve discrepancies between prototype and production systems.
Contributed to enhancements of CME’s risk management tooling by analyzing market and correlation data to surface key stress-scenario drivers, preparing price time-series data in SQL and Python, and working with QA to resolve discrepancies between prototype and production systems.