At Apple, I guide search, discovery, and order-experience roadmap decisions through rigorous experimentation, causal inference, and machine learning for relevance and ranking.
I've built SQL and Python pipelines from raw event streams, created self-service dashboards, and developed simulations that help leadership evaluate marketplace and policy investments. My work connects offline evaluation metrics to real-world improvements in conversion, order rate, and GTV.
Previously at HubSpot, I owned marketing channel measurement using multi-touch attribution, geo holdouts, incrementality methods, and LTV modeling to inform acquisition strategy and budget allocation.
My background at Bloomberg strengthened my product analytics foundation through Python and SQL workflows, A/B testing, dashboarding, data-quality automation, and close collaboration with engineering teams. I also mentor others on experiment design, causal methods, SQL best practices, and clear technical communication.
