Staff Data Scientist, Clinical Performance
About The Position
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.
Requirements
- 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).
- Excellent communication skills, with the ability to translate complex statistical concepts into actionable insights for technical and non-technical stakeholders.
Nice To Haves
- Experience with SageMaker is a plus.
- Experience working with healthcare quality measures (eCQMs, HEDIS, MSSP, ACO REACH, or similar CMS programs) is preferred.
Responsibilities
- 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
Benefits
- Eligible for a discretionary performance bonus and equity options
- Competitive benefits package
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What This Job Offers
Job Type
Full-time
Career Level
Senior
