Partner with product managers during problem sizing to quantify opportunity areas, establish baselines, and define what success looks like before any solution is built

Conduct exploratory data analysis during model development, profiling data quality, understanding distributions, identifying anomalies, and helping data scientists understand what the data is actually telling them

Build and maintain the feature datasets and data definitions that model development depends on, ensuring the analytical foundation is sound before anyone builds on it

Design and own the measurement frameworks that track AI performance in production, going beyond adoption to capture clinical and operational impact

Dig into Epic, Clarity, Caboodle, and other enterprise data sources to answer hard questions about how AI initiatives are performing and where they need to improve

Identify and resolve data quality issues at the source, working with clinical and operational teams to address problems that would otherwise degrade model performance silently

Turn complex analytical findings into clear narratives and visualizations that help product and strategy leaders make faster, better decisions

Establish and maintain data standards and documentation that make analytical work reusable and trustworthy across the portfolio