What You'll Do

You will lead the design and implementation of advanced causal inference and statistical frameworks to measure and forecast the effectiveness of Pearl’s clinical products and operational services. - Architect Causal Frameworks: Design and build the scalable systems required to conduct rigorous impact analyses, moving beyond simple correlations to isolate the true "Pearl Effect" on patient populations. Set the technical bar for how we handle complex data challenges, including non-randomized treatment assignment, selection bias, and compounding intervention effects. - Forecast Quality & Performance: Develop predictive models to issue forecasts for clinical quality measures (including eCQMs in MSSP and claims-based measures in REACH and LEAD). This includes establishing the "status quo" baseline to accurately quantify Pearl's incremental impact. - Partner with other Staff Data Scientists to refine and validate patient risk models, ensuring that "rising acuity" signals are integrated effectively into our performance evaluation loops. - Partner with Engineering and Analytics to build robust data pipelines and ML infrastructure that support automated, repeatable performance measurement. - Collaborate with Product and Clinical Operations leaders to turn 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 our statistical models — leveraging automation for continuous training, deployment, performance monitoring, and proactive model refreshes. #