Senior Data Scientist - Real World Data

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. 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. About the Position 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. 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. Responsibilities 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. About You You have 5+ years of