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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, Sam Altman, John Doerr, Spark Capital, SV Angel Growth, and others.
You can read more at the following links:
Our Vision for AI in Pharma
Our Current Drug Portfolio
Our Technology & Platform
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.
We're looking for a Senior Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help transform Real World Data (RWD)—spanning electronic health records, claims, and other longitudinal patient data sources—into structured, analytics-ready assets. In this role, you'll be partnering closely with our Data Science team not only to model and transform data, but also to actively analyze it: answering research questions, generating evidence, and supporting scientific decision-making across our drug portfolio.
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New York City, USA
New York City, USA
Boston, USA
New York City, USA
New York City, USA
New York City, USA
New York City, USA
Boston, USA
New York City, USA
Boston, USA
Boston, USA
This position sits at the intersection of healthcare data engineering, real-world evidence analysis, and generative AI. While a strong foundation in building reliable, scalable pipelines is essential, you'll be equally expected to roll up your sleeves and work directly with the data—constructing cohorts, running analyses, and translating findings into actionable insights for scientific and business stakeholders.
The ideal candidate is a hybrid of data engineer and applied scientist: someone who can build the infrastructure and then use it, with familiarity in RWD study design, GenAI fluency (e.g., LLM-based entity extraction, summarization, classification), and strong technical expertise with modern data tooling. You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization.
You have 5+ years of experience in data engineering, ideally with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets.
You have experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understand the modalities found within RWD — billing claims, lab results, visit notes.
You're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows.
You have experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction.
You have a solid understanding of the causal inference frameworks such as potential outcomes and target trial emulation.
You have working familiarity with real-world evidence study design concepts—such as active comparator new user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms—sufficient to partner effectively with Data Scientists on causal inference workflows.
You value clarity, documentation, and structured thinking—especially when working with complex healthcare data.
You have hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster.
You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
Bonus: You've worked in regulated or privacy-sensitive data environments and are familiar with governance models for PHI or sensitive data.
Bonus: You have prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understand the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing.
Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas. These positions will follow a hybrid work model with 1-3 days required at the office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please only apply if you reside in these locations or are willing to relocate.
AI-native pharmaceutical company acquiring and developing clinical-stage drugs to bring new treatments to patients faster.
Visit company websiteJobs and hiring trendsUSD 204500-267000 yearly / year
Full-time
Senior · 5+ years experience
Hybrid
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