Principal Data Scientist, Health Informatics

Waymark

4d • $124,160 - $229,000

About The Position

Waymark is seeking a Principal Data Scientist to own clinical data as a first-class input to modeling and to bring senior ML/AI and health economics judgment to our core data science products. As Waymark scales across health plan and health system partners, clinical data quality directly determines model accuracy. We need a senior owner accountable for data quality, normalization, and clinical validity across claims, EHR, and ADT. This role sits at the intersection of clinical data expertise, applied ML/AI, and health economics methods. You will own the clinical data strategy that enables our modeling, defining how EHR and ADT data, across formats including FHIR, HL7v2, and C-CDA, should be structured, normalized, and validated as modeling inputs, with hands-on fluency in how these systems are structured and what the data actually represents clinically. You will build and ship production models that advance our existing machine learning and generative AI products, and operate as a senior technical leader, making architectural trade-offs, aligning data science, engineering, product, and clinical stakeholders, and raising the technical bar of the team. This is a highly versatile role for someone who is equally fluent in clinical terminologies and production ML, and who can move work from prototype to deployment with rigor and speed.

Requirements

• Healthcare Data Expertise: 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.
• Standards Fluency: Working experience with healthcare data standards and exchange formats — FHIR, HL7v2, and C-CDA.
• Education: Master's degree in Data Science, Biostatistics, Health Informatics, Computer Science, or a related field.
• Python Proficiency: 7-8+ years of hands-on experience in Python, including data science and ML libraries.
• Applied ML/AI Experience: 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.
• Project Ownership: 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.
• Health Economics & Outcomes Methods: 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.

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. Hold the bar for clinical accuracy and completeness across all three sources.
• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal