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THE OPPORTUNITY As a Staff Data Scientist on the Clinical Performance team, you will be the head developer of the predictive models that power Pearl Health’s impact on the American healthcare system. Reporting to the Senior Director of Clinical Performance, you will tackle the massive challenge of predicting patient behavior and outcomes and building risk stratification and forecasting engines to improve quality performance across various value-based care programs. In addition to the focus on machine learning and forecasting, you will be a key player in untangling overlapping clinical interventions to isolate exactly what drives better patient outcomes and financial sustainability. You will play a defining role in building the feedback system that guides our company strategy and validates our mission to empower primary care providers.   WHO WE ARE Pearl Health is dedicated to empowering primary care providers, health systems, and physician-led networks to succeed in the shift to value-based care. Our platform delivers the technology, financial tools, and expert services that enable practices to provide more proactive, effective care to their Medicare patients, ultimately lowering costs and improving health outcomes. Founded in 2020, we are a team of healthcare and technology innovators backed by premier investors like Andreessen Horowitz, Viking Global Investors, and AlleyCorp. We partner with thousands of providers across 44 states to build a more sustainable future for American healthcare.   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. - Develop and deploy ML models to predict patient outcomes and forecast clinical quality measures. - Design and build the scalable systems required to conduct 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 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   WHAT YOU'LL BRING - Master's degree or higher in Statistics, Economics, Biostatistics, Epidemiology, or a related quantitative field, with 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, including techniques such as difference-in-differences, propensity score matching, or synthetic controls. - Expert proficiency in Python and SQL, with experience developing scalable data science solutions in cloud environments (AWS, Snowflake, dbt). Experience with SageMaker is a plus. - Excellent communication skills, with the ability to translate complex statistical concepts into actionable insights for technical and non-technical stakeholders. - Experience working with healthcare quality measures (eCQMs, HEDIS, MSSP, ACO REACH, or similar CMS programs) is preferred. OUR VALUES 🤝 Collaborate to Innovate: We believe the best solutions arise from intelligent teamwork. We trust the expertise of our teammates and pursue opportunities to learn and grow from each other. By embracing diverse perspectives and encouraging authenticity, we create and evangelize groundbreaking health solutions. 🗣️ Trust Through Transparency: We prioritize transparency in all our interactions, ensuring that employees, patients, clinicians, and partners have access to the information they need to make informed decisions. Integrity is at the core of how we operat