pearlhealth - Staff Data Scientist, Clinical Performance
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. • 🤝 Collaborate to Innovate: We believe the best solutions arise from intelligent teamwork. We trust the expertise of our teammates and pursue opportunities to learn and grow from each other. By embracing diverse perspectives and encouraging authenticity, we create and evangelize groundbreaking health solutions. • 🗣️ Trust Through Transparency: We prioritize transparency in all our interactions, ensuring that employees, patients, clinicians, and partners have access to the information they need to make informed decisions. Integrity is at the core of how we operate. • ❤️ Serious Impact, Big Heart: We go above and beyond to empower proactive, patient-centered care — and we celebrate every step forward. Humor and positivity fuel our creativity and strengthen relationships.
Responsibilities
• 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. • Architect Causal Frameworks: 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. Set the technical bar for how we handle complex data challenges, including non-randomized treatment assignment, selection bias, and compounding intervention effects. • Forecast Quality & Performance: Develop predictive models to issue forecasts for clinical quality measures (including eCQMs in MSSP and claims-based measures in REACH and LEAD). This includes establishing the "status quo" baseline to accurately quantify Pearl's incremental impact. • Collaborate on Patient Risk: 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. • Lead Technical Execution: Partner with Engineering and Analytics to build robust data pipelines and ML infrastructure that support automated, repeatable performance measurement. • Translate Insights for Action: Collaborate with Product and Clinical Operations leaders to turn complex statistical findings into actionable narratives that influence product roadmaps and practice coaching. • Automate Model Lifecycles with AI Agents: Architect and oversee AI-driven agents that autonomously manage the end-to-end lifecycle of our statistical models — leveraging automation for continuous training, deployment, performance monitoring, and proactive model refreshes.
Benefits
• The expected offer for this role includes the following components: • Base Salary Range: $160,000 -$200,000 per year • Additional Compensation: Eligible for a discretionary performance bonus and equity options • Benefits: We offer a competitive benefits package. More on our careers page https://www.pearlhealth.com/careers. • Final compensation will be determined by a variety of factors, including relevant skills, experience, labor market conditions, and location. • AGENCY SUBMISSIONS • We are not currently working with contingency search firms. If a resume is submitted to any Pearl Health employee by a third party without a valid written and signed search agreement, it will become the property of Pearl Health and no fee will be paid, irrespective of whether the candidate is hired. • THE INTERVIEW PROCESS • While steps may vary by role, you can typically expect: • Recruiter Screen: Introductory call to discuss background and motivation • Subject Matter Expert Interview: Deep-dive conversation with the Hiring Manager • Panel Interview: Meetings with teammates and cross-functional partners • Executive Interview: Final conversation(s) with 2 members of our leadership team
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