Job Overview
Location
San Francisco, NYC, Boston, or Remote
Job Type
Full-time
Category
Data Science
Date Posted
April 24, 2026
Full Job Description
đź“‹ Description
- • As a Staff Data Scientist on the Clinical Performance team at Pearl Health, you will serve as the lead architect of methodologies that prove the company’s impact on the American healthcare system by isolating the true drivers of better patient outcomes and financial sustainability.
- • 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, including architecting causal frameworks, forecasting quality and performance, collaborating on patient risk models, leading technical execution with engineering and analytics teams, translating insights into actionable narratives for product and clinical operations, and automating model lifecycles with AI agents.
- • Pearl Health is a mission-driven company focused on empowering primary care providers through value-based care, where data science plays a defining role in guiding company strategy and validating impact on patient care and healthcare system sustainability.
- • In this role, you will deepen your expertise in causal inference, predictive modeling, and scalable ML infrastructure while influencing high-stakes healthcare decisions, publishing-quality work, and shaping the technical foundation of a growing health tech organization.
🎯 Requirements
- • 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.
- • 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), with deep recent experience with AWS Sagemaker 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 (Nice-to-Have): Deep familiarity with eCQMs, HEDIS, or claims-based quality measures within MSSP, ACO REACH, or similar CMS programs.
🏖️ Benefits
- • Competitive Base Salary Range: $160,000 - $200,000 per year.
- • Additional Compensation: Eligible for a discretionary performance bonus and equity options.
- • Comprehensive Benefits Package: Competitive health, wellness, and retirement benefits (details available on Pearl Health careers page).
- • Mission-Driven Work: Opportunity to directly impact patient outcomes and healthcare system sustainability through innovative data science in primary care.
- • Collaborative & Transparent Culture: Work in an environment that values teamwork, transparency, authenticity, and continuous learning.
- • Professional Growth: Engage in thought leadership opportunities, including potential for peer-reviewed research and industry conference presentations.
Skills & Technologies
About Pearl Health, Inc.
Pearl Health is a technology company that provides data-driven tools to primary care physicians for managing value-based care contracts. Its platform aggregates and analyzes claims, clinical, and social data to identify high-risk patients, surface actionable insights, and track performance against quality and cost metrics. By offering workflows, benchmarking, and financial reconciliation, the company helps independent practices shift from fee-for-service to risk-sharing arrangements with Medicare Advantage and other payers, aiming to improve patient outcomes while increasing physician revenue.
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