Associate Director/Director, Data Science, EHR AI

The role

You will lead the EHR AI team to build capabilities for learning from real-world data. You will develop models using electronic health records, claims, and other sources to shape drug acquisition, indications, and trial design.

What you will do

• Lead the EHR AI team, set technical strategy, hire and develop the team, and own delivery
• Build and validate predictive models on longitudinal EHR and claims data
• Evaluate, adapt, and fine-tune EHR foundation models
• Combine predictive modeling with causal approaches to identify drug repurposing and indication expansion
• Deliver EHR and RWE analyses for Business Development diligence and R&D programs
• Partner with Engineering and Product to turn prototypes into reusable capabilities
• Set standards for phenotype definition, validation, and reproducibility

Required qualifications

• PhD in computer science, biomedical informatics, statistics, epidemiology, or a related quantitative field
• 5+ years of industry or research experience building machine learning models on EHR data, including linked claims or other real-world data
• 2+ years of people management, leading data scientists or ML researchers as direct reports
• Working knowledge of how EHR and claims data are generated and structured
• Fluency with clinical vocabularies and common data models, including ICD-10, SNOMED CT, LOINC, RxNorm, ATC, procedure coding systems, and the OMOP CDM
• Familiarity with common uses of EHR data and RWE in drug development
• Hands-on proficiency in Python and modern ML frameworks
• High comfort with LLMs, including coding agents
• Strong communication skills with clinical, scientific, and executive audiences

Nice to have

• Experience pretraining, fine-tuning, or rigorously benchmarking EHR foundation models or LLM-based approaches to patient timelines
• Causal inference on observational health data: target trial emulation, propensity score methods, instrumental variables, doubly robust or ML-based estimators
• Use of LLMs for clinical text, such as phenotyping or information extraction from clinical notes
• Experience with OHDSI tools and network studies, or with large linked EHR and biobank resources
• Track record of EHR models that influenced acquisition, licensing, trial design, or regulatory decisions
• Exposure to health economics and outcomes research (HEOR)

Benefits

• equity
• comprehensive benefits
• generous perks

Before you apply

• Please apply only if you reside in New York City, Boston metro areas, Research Triangle (NC), or San Francisco Bay Area or are willing to relocate

About the company

Formation Bio is a tech and AI driven pharma company that builds technology platforms, processes, and capabilities to accelerate drug development and clinical trials.