Brooklyn, New York, United States
I'm a credentialed actuary (ASA) with 6+ years building production-grade Python and SQL systems for health insurance financial modeling — Medicare Advantage, risk adjustment, claims reserving, pharmacy rebate valuation, and value-based care arrangements. At Oscar Health, I architected the data pipelines and schema powering a $1.5B ACA risk adjustment forecasting model, modernizing a spreadsheet-based process into something automated, auditable, and less error-prone. The forecast estimates fed directly into executive financial planning — informing how much capital the company needed to keep on hand. I'm pursuing an M.S. in Computer Science (ML specialization) at Georgia Tech because I'm genuinely fascinated by where AI and ML are heading. I want to be someone who can apply those tools, not just know about them. Looking for: technically demanding roles where complex model results need to get into the right hands and actually inform decisions — not just sit in a dashboard nobody reads.
Architected and built end-to-end ETL pipelines and data table schema (Python, SQL, BigQuery) powering a $1.5B ACA risk adjustment forecasting model — modernizing a spreadsheet-based process into an automated, auditable platform used in executive financial planning. Built interactive dashboards visualizing risk scores, disease prevalence, and financial exposure across 20+ states, enabling leadership to treat risk adjustment as a strategic forecasting capability rather than a compliance exercise.
Built financial models for pharmacy rebate contracts offsetting $250M+ in annual drug expenses, translating complex contract language into valuation logic and improving forecast accuracy to within 5% of actual invoice results. Collaborated across Finance, Legal, Data Science, and Operations to model cash flows and monitor emerging experience for high-utilization drugs — providing a reliable foundation for capital planning and pricing decisions.
Built the financial reporting, analytics, and modeling framework for Oscar's Medicare Advantage launch — owning IBNR reserves, shared savings, and revenue forecasting end-to-end, achieving forecast accuracy within 0.5% of actual results. Presented financial results regularly to external provider executives, aligning on shared-risk contract assumptions and translating model outputs into decisions for a non-technical audience.
Supported Medicare Advantage risk adjustment forecasting and analytics for a $1.2B line of business — developing BI reports on risk scores, diagnosis recapture rates, and provider performance that contributed to a 5% improvement in revenue capture. Built provider targeting cohorts for healthcare programs including chart reviews, in-home assessments, and care gap outreach — quantifying ROI and translating results into provider education on coding accuracy.