Associate Director/Director, Data Science, EHR AI

<div class="content-intro"><h3><strong>About Formation Bio</strong></h3> <p><strong><em>Formation Bio is a tech and AI driven pharma company differentiated by radically more efficient drug development.</em></strong> </p> <p>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. </p> <p>You can read more at the following links:</p> <ul> <li><a href="https://www.formation.bio/blog/our-vision-for-ai-in-pharma">Our Vision for AI in Pharma</a></li> <li><a href="https://www.formation.bio/licensing-and-pipeline">Our Current Drug Portfolio</a></li> <li><a href="https://www.formation.bio/technology">Our Technology & Platform</a></li> </ul> <p>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.</p></div><p><strong>About the Position</strong></p> <p>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.</p> <p>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.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Lead the EHR AI team within Data Science: set technical strategy, hire and develop the team, and own delivery against portfolio priorities</li> <li>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</li> <li>Evaluate, adapt, and fine-tune EHR foundation models for patient representation, and determine where they outperform simpler baselines</li> <li>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</li> <li>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</li> <li>Partner with Engineering and Product to turn prototypes into reusable, self-serve capabilities, and with data partners on dataset selection and fitness for use</li> <li>Set standards for phenotype definition, validation, and reproducibility across all EHR and RWD work</li> </ul> <p><strong>About You</strong></p> <p><strong>Required Qualifications</strong></p> <ul> <li>PhD in computer science, biomedical informatics, statistics, epidemiology, or a related quantitative field (required)</li> <li>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</li> <li>2+ years of people management, leading data scientists or ML researchers as direct reports</li> <li>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</li> <li>Fluency with clinical vocabularies and common data models, including ICD-10 and its national modi