Senior / Principal Data Scientist, EHR AI

What you'll do

• Train, validate, and benchmark models for disease progression, treatment response, and patient outcomes; define cohorts and phenotypes.
• Apply causal methods and deliver RWE analyses to Business Development and R&D, communicating uncertainty and bias.
• Write reproducible code and collaborate with Engineering and Product; at Principal level, lead a modeling workstream and mentor scientists.

What they ask for

• PhD in computer science, biomedical informatics, statistics, epidemiology, or a related quantitative field.
• 2+ years of industry or research experience building machine learning models on EHR data, including linked claims or other real-world data, with substantiated results.
• Strong Python and deep learning expertise; familiarity with data lakes and distributed compute for large clinical datasets.
• Working knowledge of EHR and claims data, clinical vocabularies and common data models including OMOP CDM, and rigorous model evaluation.

Nice to have

• Experience with EHR foundation models or LLM-based patient timelines, causal inference, or LLM use for clinical text.
• Experience with OHDSI, linked EHR and biobank resources, pharma/biotech/health tech, or HEOR.

Pay

$185,500 - $232,000 (USD, total compensation range, Senior); $213,500 - $267,000 (USD, total compensation range, Principal)

Tools

Python, OMOP CDM, ICD-10, SNOMED CT, LOINC, RxNorm, ATC, LLMs

AI focus

Develop and evaluate predictive models, including EHR foundation models, and use LLMs for clinical data and text; apply causal methods to identify drugs that may modify disease course.