Principal Data Scientist, Health Informatics

Waymark

Principal Data Scientist at Waymark responsible for clinical data modeling and machine learning in healthcare. Owning clinical insights from EHR and claims data to enhance model accuracy.

Posted 6/8/2026full-timeRemote • California, District of Columbia, Florida, Illinois, Maryland, Massachusetts, Minnesota, New York, North Carolina, Oregon, Texas, Virginia, Washington • 🇺🇸 United StatesLead💰 $128,000 - $229,000 per yearWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonML/AIrisk stratificationcare gap predictiontreatment effect estimationdata normalizationdata validationhealth economicsutilization measurementcost attribution

Soft Skills

project managementmentoringstakeholder alignmentarchitectural trade-offscommunication