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