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Company Overview
A leading healthcare technology company is leveraging artificial intelligence and real-world data to advance precision medicine and transform clinical care. Its proprietary platform integrates diverse datasets to provide actionable insights for physicians, supporting the delivery of the right treatments to the right patients at the right time. The Outcomes Research team collaborates with pharmaceutical, biotech, and academic partners to deliver best-in-class data analysis, methodological guidance, and research insights in real-world evidence (RWE) and health economic outcomes research (HEOR).
Summary
The Data Scientist II will design, execute, and interpret real-world studies to generate insights on treatment patterns, healthcare utilization, and clinical outcomes. This role requires expertise in pharmacoepidemiology, HEOR, and advanced data analysis, along with the ability to collaborate with internal and external stakeholders to translate findings into actionable, high-quality insights.
Responsibilities
Lead and execute HEOR and RWE projects, including outcomes analysis, treatment patterns, and healthcare resource utilization, in partnership with pharmaceutical, academic, and other external collaborators
Represent the Outcomes Research function, collaborating with internal and external stakeholders in study design, analysis, interpretation, and publication of real-world studies
Solve complex analytical problems using appropriate epidemiologic and health economic methodologies
Partner with interdisciplinary teams of scientists, engineers, and product developers to translate research into actionable insights
Stay updated on advances in RWE, including causal inference and pharmacoepidemiologic methods
Build analytical infrastructure, including reusable code, templates, and workflows, to improve efficiency and quality across engagements
Comply with all applicable regulations, data governance, and internal procedures related to real-world data use and reporting
Required Experience
Advanced degree (Master’s with 2+ years experience or equivalent) in data science, bioinformatics, biostatistics, epidemiology, immunology, public health, or a related quantitative field
Demonstrated computational skills using R and SQL, applied to large-scale healthcare datasets
Preferred Qualifications
Strong data manipulation and analytical skills tailored to observational and real-world data
Deep familiarity with HEOR and RWE methodologies, including approaches to address confounding (e.g., propensity score methods, inverse probability weighting)
Experience analyzing large, complex datasets, including administrative claims, electronic health records (EHR), and clinico-genomic databases
Strong communication and presentation skills for non-technical stakeholders
Experience with time-to-event analysis and survival methodologies
Oncology experience and familiarity with outcomes related to cancer genetics, immunology, or molecular biology
Hands-on experience contributing to regulatory submissions to FDA or other health authorities
Collaborative, self-driven, and high-integrity work ethic with attention to detail
Nice to Have
Experience with version control (e.g., Git) and software testing/validation
Experience in oncology Phase II-IV clinical trials or RWD/HEOR studies
Support for data science teams in model building, validation, feature engineering, and performance assessment
Client-facing or consulting experience presenting results to external stakeholders
Disclaimer
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