My work connects deep statistical training with real-world execution. My background spans academic research, observational methods, healthcare analytics, and product collaboration, and today my focus is on building teams, systems, and study practices that make evidence generation more reliable, scalable, and useful.

I'm especially interested in the operational side of strong science: how teams design regulatory-grade real-world evidence studies, create durable analytic workflows, and use tools like R, Shiny, and emerging AI capabilities thoughtfully to improve both internal efficiency and product direction. The work I find most meaningful sits in the real-world evidence space, especially where scientific rigor, cross-functional leadership, and practical decision-making need to come together at scale in settings that influence biopharma strategy and development.