Principal Data Scientist, Health Informatics

Responsibilities

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds.
• Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Requirements

• Deep, hands-on fluency with claims, EHR, and ADT data, and strong command of clinical terminologies (ICD-10, SNOMED CT, LOINC, RxNorm, CPT/HCPCS) and value set curation.
• Working experience with healthcare data standards and exchange formats — FHIR, HL7v2, and C-CDA.
• Master's degree in Data Science, Biostatistics, Health Informatics, Computer Science, or a related field.
• 7-8+ years of hands-on experience in Python, including data science and ML libraries.
• Demonstrated ability to build, validate, and deploy production ML models on healthcare data, with end-to-end ownership from development through deployment and maintenance in a live environment.
• Experience with ML pipelines, model versioning, and reproducible workflows at scale.
• Proven ability to manage complex technical projects independently, align multiple stakeholders, and deliver on timelines.

Nice-to-Haves

• PhD in health informatics, statistics, data science, or computer science.
• Experience integrating EHR/HIE data via TEFCA, CommonWell, or comparable networks.
• Experience with risk adjustment, utilization and cost measurement, and quasi-experimental evaluation.
• Familiarity with MLOps best practices including experiment tracking and model registry (e.g. MLflow), CI/CD for ML pipelines, feature stores, and workflow orchestration tools such as SageMaker Pipelines.
• Prior experience building on Medicaid or dual-eligible populations.
• Peer-reviewed publications in healthcare ML, AI, biostatistics, or health economics.

Compensation & Benefits

• Stock Options: Opportunity to invest in the company's growth.
• Work-from-Home Stipend: A dedicated stipend for your first year to help set up your home office.
• Medical, Vision, and Dental Coverage: Comprehensive plans to keep you and your family healthy.
• Life Insurance: Basic life insurance to give you peace of mind.
• Paid Time Off: 20 vacation days, accrued over the year, plus 11 paid holidays.
• Parental Leave: 16 weeks of paid leave for birthing parents after six months of employment, and 8 weeks of bonding leave for non-birthing parents.
• Retirement Savings: Access to a 401(k) plan with a company contribution, subject to a vesting schedule.
• Commuter Benefits: Convenient options to support your commute needs.
• Professional Development Stipend: A dedicated stipend supports professional development and growth.