Associate Director/Director, Data Science, EHR AI

About Formation Bio

Formation Bio is a tech and AI driven pharma company differentiated by radically more efficient drug development.  

Advancements in AI and drug discovery are creating more candidate drugs than the industry can progress because of the high cost and time of clinical trials. Recognizing that this development bottleneck may ultimately limit the number of new medicines that can reach patients, Formation Bio, founded in 2016 as TrialSpark Inc., has built technology platforms, processes, and capabilities to accelerate all aspects of drug development and clinical trials. Formation Bio partners, acquires, or in-licenses drugs from pharma companies, research organizations, and biotechs to develop programs past clinical proof of concept and beyond, ultimately helping to bring new medicines to patients. The company is backed by investors across pharma and tech, including a16z, Sequoia, Sanofi, Thrive Capital, John Doerr, Spark Capital, SV Angel Growth, and others. 

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At Formation Bio, our values are the driving force behind our mission to revolutionize the pharma industry. Every team and individual at the company shares these same values, and every team and individual plays a key part in our mission to bring new treatments to patients faster and more efficiently.

About the Position

As the Associate Director or Director leading the EHR AI team within Data Science at Formation Bio, you will build and lead our capability for learning from real-world data (RWD): electronic health records (EHR) together with claims and other real-world sources. Your models will turn longitudinal patient data into real-world evidence (RWE) that shapes which drugs we acquire, which indications we pursue, and how we design trials. You will set the technical direction for machine learning on EHR and other real-world data, including the evaluation and adaptation of EHR foundation models, and apply it to the questions that move our portfolio: which approved drugs alter the course of disease, which patients progress and when, and how a trial population is likely to respond. You will work with large-scale, longitudinal EHR data from health systems, linked to claims, clinical notes, and imaging, alongside biobank-scale genetic and molecular data.

You will report to the VP of Data Science and partner primarily with Business Development and R&D; on the technology side, you will work closely with Engineering and Product. This role owns the modeling, the scientific standards, and the translation of results into decisions. The ideal candidate has built and validated models on EHR and claims data, knows the data well enough to anticipate where it misleads, and can explain what a model means for a deal or a trial to executives and clinicians.

Responsibilities

• Lead the EHR AI team within Data Science: set technical strategy, hire and develop the team, and own delivery against portfolio priorities
• Build and validate predictive models on longitudinal EHR and claims data (disease progression, time-to-event outcomes, patient-level treatment response) with evaluation designs that hold up under temporal and cross-site shift
• Evaluate, adapt, and fine-tune EHR foundation models for patient representation, and determine where they outperform simpler baselines
• Combine predictive modeling with causal approaches such as target trial emulation to identify approved drugs that modify disease course, generating repurposing and indication expansion candidates
• Deliver fast, rigorous EHR and RWE analyses for Business Development diligence and R&D programs: cohort definition and feasibility, natural history, patient segmentation, and endpoint and trial design support
• Partner with Engineering and Product to turn prototypes into reusable, self-serve capabilities, and with data partners on dataset selection and fitness for use
• Set standards for phenotype definition, validation, and reproducibility across all EHR and RWD work

About You

Required Qualifications

• PhD in computer science, biomedical informatics, statistics, epidemiology, or a related quantitative field (required)
• 5+ 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
• 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: 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
• Hands-on proficiency in Python and modern ML frameworks; you can review code and run experiments yourself
• High comfort with LLMs, including coding agents, as part of your daily work
• Strong communication skills with clinical, scientific, and executive audiences

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)
• Track record of EHR models that influenced acquisition, licensing, trial design, or regulatory decisions
• Exposure to health economics and outcomes research (HEOR)

Total Compensation Range: Associate Director $213,500 - $267,000; Director $245,500 - $307,000

Compensation  Individual compensation is determined by several factors, including role scope, geographic location, and skills & experience. Your offer will reflect where you fall within the range based on these considerations. In addition to base salary, we offer equity, comprehensive benefits, and generous perks.  If the posted range doesn't match your expectations, we still encourage you to apply!

Where We Hire Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please apply only if you reside in these locations or are willing to relocate .

Equal Opportunity Formation Bio is committed to building a diverse and inclusive team. We are an equal opportunity employer and welcome candidates from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender identity or expression, sexual orientation, age, disability, genetic information, marital status, military or veteran status, or any other characteristic protected by federal, state, or local law.

We've seen a rise in recruiting impersonation scams across the industry, where fake profiles pose as recruiters aiming to trick candidates into sharing personal and financial information through illegitimate interviews and job offers. All legitimate recruiting communications from Formation Bio will come from a @formation.bio email address. If you receive outreach from someone claiming to work at Formation Bio through any other domain or channel, please disregard it and do not share any personal information.