Staff Data Scientist, Clinical Performance

What You'll Do

• Lead the design and implementation of advanced causal inference and statistical frameworks to measure and forecast the effectiveness of clinical products and operational services.
• Develop and deploy ML models to predict patient outcomes and forecast clinical quality measures.
• Design and build scalable systems for rigorous impact analyses to isolate the true "Pearl Effect" on patient populations.
• Partner with Engineering and Analytics to build robust data pipelines and ML infrastructure supporting automated, repeatable performance measurement.
• Collaborate with Product and Clinical Operations leaders to translate complex statistical findings into actionable narratives that influence product roadmaps and practice coaching.
• Architect and oversee AI-driven agents that autonomously manage the end-to-end lifecycle of statistical models.

What You'll Bring

• Master's degree or higher in Statistics, Economics, Biostatistics, Epidemiology, or related quantitative field.
• 8+ years of experience in data science or quantitative analytics.
• Strong experience building and deploying machine learning models for prediction, forecasting, or risk modeling.
• Proven expertise in causal inference methods (difference-in-differences, propensity score matching, synthetic controls).
• Expert proficiency in Python and SQL; experience developing scalable data science solutions in cloud environments (AWS, Snowflake, dbt). SageMaker experience is a plus.
• Excellent communication skills to translate complex statistical concepts into actionable insights for diverse stakeholders.
• Experience working with healthcare quality measures (eCQMs, HEDIS, MSSP, ACO REACH, or similar CMS programs) is preferred.

What We Offer

• Base Salary Range: $160,000 - $200,000 per year.
• Eligible for discretionary performance bonus and equity options.
• Competitive benefits package.