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Pearl Health

Staff Data Scientist, Clinical Performance

Leads design of causal inference frameworks and predictive models to measure clinical interventions' impact on patient outcomes and quality measures in value-based care. Requires 8+ years experience, advanced degree in quantitative field, and expertise in Python/SQL for production DS systems.

160k – 200kSan Francisco, CAData ScienceRemote8+ YOE
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About the role

What You'll Do

  • Architect Causal Frameworks: Design and build scalable systems for rigorous impact analyses, handling non-randomized treatment assignment, selection bias, and compounding intervention effects.
  • Forecast Quality & Performance: Develop predictive models for clinical quality measures (e.g., eCQMs in MSSP, claims-based in REACH and LEAD), establishing baselines to quantify Pearl's impact.
  • Collaborate on Patient Risk: Partner with other data scientists to refine and validate patient risk models, integrating rising acuity signals into performance evaluation.
  • Lead Technical Execution: Partner with Engineering and Analytics to build data pipelines and ML infrastructure for automated performance measurement.
  • Translate Insights for Action: Collaborate with Product and Clinical Operations to turn statistical findings into actionable narratives.
  • Automate Model Lifecycles with AI Agents: Architect AI-driven agents for continuous model training, deployment, monitoring, and refreshes.

What You'll Bring

Must-haves:

  • Advanced Quantitative Expertise: Graduate degree (Masters or PhD) in Statistics, Economics, Biostatistics, or Epidemiology, with 8+ years in quantitative analysis.
  • Deep Causal & Statistical Literacy: Experience with causal inference (diff-in-diff, synthetic control, propensity score matching) in messy data.
  • Predictive & Forecasting Proficiency: Building time-series forecasts or risk-adjustment models.
  • Full-Stack Data Science Skills: Expert Python and SQL for production code and scalable architectures.
  • Architectural Thinking: Building scalable DS systems in cloud (AWS, Snowflake, dbt); AWS Sagemaker a plus.
  • Exceptional Communication: Explaining complex stats to non-technical audiences.

Nice-to-haves:

  • Healthcare Quality Expertise: eCQMs, HEDIS, claims-based measures in MSSP, ACO REACH.
  • Thought Leadership: Peer-reviewed research or conference presentations.

What We Offer

  • Base Salary Range: $160,000 - $200,000 per year
  • Additional Compensation: Discretionary performance bonus and equity options
  • Competitive benefits package

Skills

PythonSQLCausal InferenceDiff-In-DiffSynthetic ControlPropensity Score MatchingTime-Series ForecastingRisk-Adjustment ModelsAWSSnowflakedbtAws SagemakerML Infrastructure

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