# Staff Data Scientist, Clinical Performance

**Company:** [Pearl Health](https://hotfix.jobs/companies/pearl-health)
**Location:** Remote
**Role:** Data Science
**Salary:** $160K-$200K
**Experience:** 8+ years
**Skills:** Python, SQL, Causal Inference, Diff-In-Diff, Synthetic Control, Propensity Score Matching, Time-Series Forecasting, Risk-Adjustment Models, AWS, Snowflake, dbt, Aws Sagemaker, ML Infrastructure
**Posted:** 2026-04-23

> 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.

## Job Description

## 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

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