About the Role
The Principal Data Scientist in Health Informatics will lead efforts to transform complex clinical and claims data into reliable, model-ready inputs while ensuring methodological rigor and clinical validity across AI-driven healthcare solutions.
Responsibilities
- Ensure high standards for clinical data integrity across claims, electronic health records (EHR), and admission-discharge-transfer (ADT) systems by defining protocols for data structure, normalization, and validation used in modeling.
- Demonstrate expertise in EHR data standards such as FHIR, HL7, and C-CDA, and understand how outputs from platforms like Epic, Cerner, and Athena reflect real-world clinical information.
- Maintain rigorous expectations for clinical accuracy and completeness of data derived from payer claims, EHR systems, and real-time ADT feeds.
- Design, validate, and deploy machine learning and artificial intelligence models into production environments, including applications for risk stratification, care gap identification, treatment effect analysis, and large language models.
- Apply methodological rigor to prevent data leakage, ensure model calibration, uphold fairness, and maintain clinical plausibility in all model outputs.
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid remote with team collaboration expectations
Team
Part of the core data science team focused on health informatics and AI-driven clinical solutions
Apply health economics and outcomes methods
Convert raw healthcare data into actionable insights using risk adjustment techniques, measurement of service utilization, cost allocation strategies, quasi-experimental study designs, and outcome metrics consistent with standards from CMS, NCQA, and managed care organizations.
Advance machine and AI products
Provide senior-level modeling expertise to shape product strategy and ensure that shipped features are both clinically meaningful and methodologically robust.
Set standards and mentor
Lead architectural decisions, align cross-functional teams including data science, engineering, product, and clinical experts, and support the growth of junior data scientists to elevate overall team capability.
Available for qualified candidates
