Senior Data Scientist, Formation Bio

• 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're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows
• 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

Desirable

• We're looking for a Senior Data Scientist 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
• 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
• You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization
• Model and transform raw EHR and claims data into clean, canonical, and analytics-ready datasets using SQL, Python, and clinical standards like OMOP
• Build and manage scalable data pipelines using Dagster for orchestration, dbt for transformation, and Snowflake as the primary compute and storage engine
• Conduct hands-on RWD analyses to answer scientific and strategic research questions—including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness
• Partner with Data Scientists and clinical leads to design and execute observational studies, translating scientific questions into well-structured, reproducible analyses
• Implement data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and trustworthy for downstream research and product use
• Apply Generative AI techniques within transformation and analysis layers to accelerate data structuring and insight generation
• Communicate findings clearly to both technical and non-technical stakeholders, including summaries for portfolio teams and leadership