Staff Data Scientist, Clinical Performance

🕒 April 24

🏄 California, New York – Remote

🏄 California – Remote 🗽 New York – Remote

💵 $160k - $200k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Pearl Health

WebsiteLinkedInAll Job Openings

51 - 200 employees

⚕️ Healthcare Insurance

☁️ SaaS

💸 Finance

💰 $18M Series A on 2021-09

Healthcare Insurance • SaaS • Finance

Pearl Health is a company dedicated to transforming primary care through value-based models. Their mission is to democratize access to value in healthcare, emphasizing quality care that rewards providers for improving patient health outcomes. Pearl Health offers data-driven insights, technology solutions, and hands-on support to primary care providers, helping them improve patient outcomes, reduce costs, and enhance their practice operations. Through strategic partnerships and their innovative Pearl Platform, they empower care providers to focus on patient care while stabilizing finances and sharing in the savings generated from quality care.

📋 Description

• 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.