About me

Adept at transforming ambiguous business problems into data-driven solutions that automate the insight-delivery process. Strong proficiency in Python, R, and SQL with a focus on testing hypotheses via A/B test, observational causal inference, and advanced experimental methods.

I’m a data science leader - with a social science background - experienced in statistical modeling, experimentation, and building scalable data products. My recent work focuses on data-driven fraud prevention, anomaly detection modeling, and combatting AI-automated threats to survey research. My previous work provided broad, Core Data Science support for a growth-stage company - including AI-automation of data-informed workflows, anomaly detection for fraud prevention, and isolating causal estimates in a dynamic two-sided marketplace.

In 2021, I received a PhD in Political Science from Washington University in St. Louis, with concentrations in statistical methods and American politics. My graduate research analyzed political communication on social media, and aimed to understand how politicians use these tools to influence and adapt to their constituents.