Senior / Principal Data Scientist, EHR AI

About Formation Bio Formation Bio is an AI-native pharma company differentiated by radically more efficient drug development. The company has built proprietary technology platforms to optimize critical aspects of clinical drug development, enabling more efficient trial design, faster trial completion, and higher quality trial data. Formation Bio acquires clinical-stage drugs from pharmaceutical and biotech companies with the goal to develop assets faster, unlock greater value per program and ultimately accelerate access to new treatments for patients. 2025 CNBC Disruptor 50 2025 Fierce 15 Award 2024 Endpoints 11 Award

Formation Bio

Bringing new treatments to patients faster and more efficiently.

F

formation.bio

51-200 employees

Biotechnology Research

New York, NY

2013

Privately Held

48k LinkedIn followers

• Our Vision for AI in Pharma
• [https://www.formation.bio/blog/our-vision-for-ai-in-pharma]
• Our Current Drug Portfolio [https://www.formation.bio/licensing-and-pipeline]
• 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.
• 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

Required

• 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
• 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

• 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
• 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
• 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.

• Equity
• Comprehensive benefits
• Generous perks
• Hybrid flexibility