Pearl Health is seeking a Staff Data Scientist to lead the development of research capabilities within the Virtual Research Data Center (VRDC) to advance understanding of provider and practice performance, which directly informs the pricing of value-based care contracts. The role aims to build the analytics backbone for research, deliver advanced financial and utilization metrics, and synthesize findings into decision tools for risk-management products.
Requirements
- Exceptional system architecture vision and software engineering skills, with proven experience designing and building scalable data science systems.
- Strong programming skills in Python and SQL, with deep experience in data manipulation, statistical modeling, and machine learning.
- Experience conducting research and building data pipelines in a distributed computing environment, particularly with Databricks and PySpark.
- Familiarity with the application of quantitative techniques to financial risk management, including Monte Carlo simulations, is a plus.
- Deep skill developing in Python.
- Strong vision for systems and software architecture to support an enterprise-quality underwriting stack.
- Experience mentoring data scientists and engineers and providing feedback on technical methods and approaches.
Responsibilities
- Build and maintain an enterprise-grade infrastructure in Python, SQL, and PySpark that will allow us to conduct cutting-edge research in the Virtual Research Data Center (VRDC) and internal systems.
- Architect the system in partnership with data scientists, finance, and other technical leaders at Pearl.
- Conduct cutting edge ML research at the intersection of healthcare and finance.
- Design greenfield systems, architect experimentation pipelines, and ultimately help improve the performance of the 500+ models that predict the financial viability of practices in value-based care.
- Implement the analytics backbone we use to perform research inside the VRDC and use that infrastructure to deliver advanced financial and utilization metrics.
- Synthesize your findings into decision tools that power our risk‑management products.
- Building the foundational infrastructure, standard operating procedures, and best practices to enable pricing/underwriting based on Medicare data.
Other
- A Ph.D. or Master's degree in a quantitative field such as statistics, applied mathematics, computer science, engineering, economics, or a related discipline, and 8+ years of experience in performing results-driven quantitative analysis.
- Healthcare data fluency, ideally with Medicare FFS claims and MA encounters, provider/ACO concepts, and value‑based care mechanics; you have the ability to translate clinical/financial pain points into measurable signals.
- Excellent communication and collaboration skills, with a track record of partnering with Finance, Product, Engineering, and other Data Scientists to turn ambiguous problems into scoped work that ships, is documented, and is adopted.
- A proven ability to propose research trajectories and lead complex projects with minimal guidance, managing multiple stakeholders while balancing deep insight with timely delivery.
- Mentorship & Talent Path: You will be a technical leader from day one by setting the bar for technical excellence and through mentorship of your peers.
