Staff Data Scientist, Clinical Performance

External

Pearlhealth · San Francisco

Full-timeRemote1mo ago

AWSdbtForecastingLeadershipPythonSnowflake

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About the role

As a Staff Data Scientist on the Clinical Performance team, you will be the lead architect of the methodologies that prove Pearl Health's impact on the American healthcare system. You will tackle the massive challenge of untangling overlapping clinical interventions to isolate exactly what drives better patient outcomes and financial sustainability. Reporting to the Senior Director of Clinical Performance, 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. While your primary focus is evaluative, you will be a key player in the broader clinical DS ecosystem, collaborating on patient risk stratification and building the forecasting engines that predict our quality performance across various value-based care programs.

Requirements

We are looking for a seasoned technical leader who can bridge the gap between high-level scientific research and scalable, production-grade data science.

Advanced Quantitative Expertise: A graduate degree (Masters or PhD) in a quantitative field such as Statistics, Economics, Biostatistics, or Epidemiology, with 8+ years of experience in results-driven quantitative analysis.

Deep Causal & Statistical Literacy: Proven experience implementing causal inference methodologies (e.g., diff-in-diff, synthetic control, propensity score matching) in real-world, messy data environments.

Predictive & Forecasting Proficiency: 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.

Full-Stack Data Science Skills: Expert-level proficiency in Python and SQL, with the ability to write production-quality code and design scalable data architectures.

Architectural Thinking: 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.

Exceptional Communication: The ability to explain the nuances of a p-value, a risk score, or an identification strategy to a non-technical audience.

Healthcare Quality Expertise: Deep familiarity with eCQMs, HEDIS, or claims-based quality measures within MSSP, ACO REACH, or similar CMS programs.

Thought Leadership: Experience publishing peer-reviewed research or presenting complex scientific findings at industry conferences.

This role might not be for you if:

You view predictive and evaluative DS as separate silos: This role requires you to understand how risk models (predicting who gets sick) and impact models (predicting if we helped) interact.

You thrive in highly structured corporate settings: We are builders of systems; you will need to navigate ambiguity and help establish the standards rather than just following a pre-existing playbook.

You view Data Science as a "black box" service: This role requires deep cross-functional partnership; you will be expected to debate strategy with Product and Operations leaders.

Our Values

🤝 Collaborate to Innovate: We believe the best solutions arise from intelligent teamwork. We trust the expertise o

Staff Data Scientist, Clinical Performance

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