San Francisco Bay Area
I’m a data scientist and healthcare analytics leader with over a decade of experience in digital health (a) evaluating the impact of programs on user engagement, clinical outcomes, and financials and (b) co-designing and launching product experiments to improve those measures. My sweet spot is connecting the dots—vertically through depth in casual inference & analytics, and horizontally across product, engineering, sales, and marketing. I help R&D teams scale experimentation and institutionalize knowledge, then translate insights into stories that resonate—whether through whitepapers, sales enablement, or direct client conversations. My core values are compassion, curiosity and community. I enjoy championing a culture of open learning and the free flow of information by bridge-building across teams and disciplines, hosting knowledge sharing forums (podcasts, mentorship programs), and 1:1 coaching. I'm most fulfilled when fixing broken systems to make them more equitable, compassionate, and just a little bit more human.
Part of the Enterprise Analytics team. By leveraging advanced analytics, expert algorithms, and real-time data, Stellar helps payors, health systems and provider networks improve quality and financial performance by prompting providers and their care staff with recommended value-based actions and real-time payments at the point of care.
Part of the Performance Analytics team, helping internal and external customers understand how Transcarent is performing and the why behind it. Transcarent makes it easy for people to access high-quality, affordable health and care.
Lead complex, cross-functional data product launches powered by statistical models, including a self-service retrospective savings analytics tool to estimate ROI across multiple product lines and drive customer retention, upsells, and satisfaction. Mentor and set standards for data team and joint practices on statistical methods and programming best practices.
Launch and development of the Omada Insights Lab brand and product. Oversaw joint R&D experimentation and marketing asset development programs. Designed care delivery experimentation platform to generate differentiated insights for proof points for sales and investments for product development. Delivered finalist round pitches to the largest employer and payer accounts. Led and grew teams composed of data scientists, analysts, and engineers.
RESEARCH ASSOCIATE (February 2014 - June 2015) Advisor: Peter Muennig, MD, MPH Coordinated statistical analyses of health policy studies in conjunction with stakeholders from NYC Department of Health and the US Census Bureau. Ran robust regression and cost-effectiveness models on high-performance computing cluster to validate a state-level health insurance study. Tools: R, Stata, Unix shell, ArcGIS, LaTeX Methods: survival analysis, multiple data imputation, factor analysis, Monte-Carlo simulation, Markov chains, distributed computing RESEARCH ASSISTANT (February 2014 - June 2015) Advisor: Jeanine Genkinger, PhD, MHS Led R programming unit. Developed interactive R programs to optimize cleaning and analysis of high-throughput epigenomics data. Ran regression analyses of experimental and observational studies relating to obesity and cancer. Tools: R, Python, SAS, Unix shell, LaTeX Methods: regression analysis, cluster analysis, machine learning TEACHING ASSISTANT (August 2014 - January 2015) Co-instructed R programming workshop. Facilitated 20+ graduate students in weekly classroom discussion and debates.
Coordinated cost-effectiveness research essential to the introduction of new legislation (Intro 0214-2014). Managed database of over 25,000 voters. Applied supervised machine learning to optimize messaging campaigns. Developed front-facing web application using NationBuilder API. Automated statistical and GIS reports to improve operations and inform policy. Tools: R, Python, MongoDB, QGIS Analytics: machine learning, predictive modeling