Senior / Principal Data Scientist, EHR AI

About the Position

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

About You

Required Qualifications

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

Preferred Qualifications

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)

Total Compensation Range: Senior $185,500 - $232,000; Principal $213,500 - $267,000