Data Scientist

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Responsibilities

• Model and transform raw electronic health record (EHR) and claims data into clean, canonical, analytics-ready datasets using SQL, Python, and clinical data standards such as OMOP.
• Build and maintain scalable data pipelines using Dagster for orchestration, dbt for data transformation, and Snowflake for compute and storage.
• Conduct hands-on real-world data (RWD) analyses addressing scientific and strategic research questions, including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness.
• Partner with Data Scientists and clinical stakeholders to design and execute observational studies, translating scientific questions into structured and reproducible analyses.
• Develop data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and reliable for research and product applications.
• Apply Generative AI techniques within data transformation and analysis workflows to accelerate data structuring and insight generation.
• Communicate analytical findings clearly to technical and non-technical stakeholders, including portfolio teams and organizational leadership.

Qualifications

• 5+ years of relevant professional experience, ideally including at least 2 years working in healthcare or life sciences with direct exposure to EHR or claims datasets.
• Experience with healthcare ontologies and biomedical schemas such as UMLS, LOINC, ICD-9/10, and MeSH.
• Understanding of real-world data modalities, including billing claims, laboratory results, and clinical visit notes.
• Strong proficiency in SQL and Python, with experience building and maintaining production-grade data pipelines supporting analytics or scientific workflows.
• Experience developing longitudinal patient cohorts from EHR or claims data, including index-date logic, washout periods, and follow-up window construction.
• Solid understanding of causal inference frameworks, including potential outcomes and target trial emulation.
• Familiarity with real-world evidence study design concepts, including active comparator new-user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms.
• Ability to partner effectively with Data Scientists on causal inference and research workflows.
• Strong documentation, communication, and structured problem-solving skills, particularly when working with complex healthcare datasets.
• Hands-on experience with modern data infrastructure and tooling such as Snowflake, dbt, and Dagster.
• Ability to balance thoughtful technical design with efficient execution.
• Experience working in regulated or privacy-sensitive data environments is a plus.
• Familiarity with governance models for protected health information (PHI) or other sensitive data is a plus.
• Experience working with commercial real-world data vendors such as Truveta, Optum, Komodo, or IQVIA is a plus.
• Understanding of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing, is a plus.

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