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# Senior / Principal Data Scientist, EHR AI

- **Company:** Formation, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $185,500.0 - $232,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Health Informatics, Information Extraction, Python (Programming Language), Machine Learning, Large Language Models, Deep Learning, Electronic Medical Records, Data Lakes, Information Technology
- **Published:** October 22, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-principal-data-scientist-ehr-ai-new-york-ny--8cac63cb-fcdc-49b5-a217-cad2d92945c5

## About the Role

Data Science, Medical Record System, Artificial Intelligence (AI), Artificial Intelligence (AI), Drug Development, Biotech and Pharmaceutical, Drug Discovery, Clinical Trial, Medical Products, Proof of Concept, * PhD in computer science, biomedical informatics, statistics, epidemiology, or a related quantitative field (required)

 * 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: peer-reviewed publications, deployed models, or externally validated performance
 * Strong Python and deep learning expertise, and familiarity with data lakes and distributed compute for large clinical datasets
 * High comfort with LLMs, including coding agents, as part of your daily work
 * Working knowledge of how EHR and claims data are generated and structured: encounters, diagnoses, procedures, medications, labs, vitals, clinical notes, coverage periods, and reimbursement records, along with coding practices, missingness, and variation across sites, payers, and countries
 * Fluency with clinical vocabularies and common data models, including ICD-10 and its national modifications, SNOMED CT, LOINC, RxNorm, ATC, procedure coding systems, and the OMOP CDM
 * Familiarity with common uses of EHR data and RWE in drug development: phenotyping and cohort definition, trial feasibility and eligibility, natural history, outcome prediction, external control arms, comparative effectiveness, and safety signal detection
 * Rigorous evaluation habits: temporal and site-held-out validation, calibration, handling of censoring, and prevention of label leakage
 * Ability to explain technical results clearly to scientific and non-technical stakeholders, * 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 (e.g., All of Us, UK Biobank)
 * Experience in pharma, biotech, or health tech applying EHR models to drug development questions
 * Exposure to health economics and outcomes research (HEOR)

## Description

As a Senior or Principal Data Scientist on the EHR AI team at Formation Bio, you will build the models that let us learn from real-world data (RWD): electronic health records (EHR) together with claims and other real-world sources. You will work hands-on with longitudinal EHR and claims data to predict how disease progresses, how patients respond to treatment, and which approved drugs change the course of disease. The data spans large-scale, longitudinal EHR from health systems, linked to claims, clinical notes, and imaging, alongside biobank-scale genetic and molecular data. Your results become real-world evidence (RWE) that feeds directly into which drugs we acquire, which indications we pursue, and how we design trials.

This is a hands-on modeling role. You will train and evaluate models, including EHR foundation models, define the cohorts and phenotypes they depend on, and turn results into clear answers for Business Development and R&D, often on tight timelines. On the technology side, you will work closely with Engineering and Product. At the Principal level, you will also lead a workstream end to end and mentor other scientists.

Responsibilities

 * Build, train, and validate predictive models on longitudinal EHR and claims data: disease progression, time-to-event outcomes, and patient-level treatment response
 * Benchmark and adapt EHR foundation models for patient representation against strong tabular and sequence baselines
 * Define and validate computable phenotypes and cohorts using the OMOP CDM and standard vocabularies, including features derived from clinical notes
 * Apply causal methods such as target trial emulation alongside predictive models to surface drugs that modify disease course
 * Answer EHR and RWE questions from Business Development diligence and R&D programs on days-to-weeks timelines, with explicit statements of uncertainty and bias
 * Write reproducible, reviewed code and work with Engineering and Product to turn prototypes into reusable tools
 * Principal level: lead a modeling workstream end to end, set its technical direction, and mentor other scientists