Staff Data Scientist, Clinical Performance
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Job Description
Staff Data Scientist leading methodologies to measure the clinical performance impact at Pearl Health. Collaborating on forecasting and risk stratification in value-based care programs.
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.
- 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.
- Develop predictive models to issue forecasts for clinical quality measures (including eCQMs in MSSP and claims-based measures in REACH and LEAD).
- 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.
Requirements:
- A graduate degree (Masters or PhD) in a quantitative field such as Statistics, Economics, Biostatistics, or Epidemiology
- 8+ years of experience in results-driven quantitative analysis.
- Proven experience implementing causal inference methodologies (e.g., diff-in-diff, synthetic control, propensity score matching) in real-world, messy data environments.
- Experience building time-series forecasts or risk-adjustment models, with a strong understanding of how to define and measure a baseline vs. an intervention effect.
- Expert-level proficiency in Python and SQL, with the ability to write production-quality code and design scalable data architectures.
- Experience building or significantly contributing to scalable data science systems and infrastructure within a modern cloud environment (AWS, Snowflake, dbt). Deep recent experience with AWS Sagemaker is a plus.
- The ability to explain the nuances of a p-value, a risk score, or an identification strategy to a non-technical audience.
Benefits:
- We offer a competitive benefits package. More on our careers page.
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