Biostatistician II Location: 3601, Main Street, Springfield, MA Institution: UMass Chan Medical School–Baystate/Baystate Health Department: Department of Healthcare Delivery and Population Sciences Position Type: Full-time/Hybrid Baystate Health and the University of Massachusetts Chan Medical School–Baystate are recruiting experienced Master’s-level Biostatisticians to join the growing Department of Healthcare Delivery and Population Sciences. This is an excellent opportunity to be part of a dynamic, mission-driven team conducting innovative research that improves health outcomes and advances health equity across diverse populations. The department provides a highly collegial, interdisciplinary academic environment focused on clinical, epidemiological, and implementation science research. Projects span a wide range of healthcare delivery topics and offer meaningful opportunities for methodological leadership and collaboration. The Quantitative Methods Core (QMC) provides statistical and data science support across a broad research portfolio, including serving as a Data Coordinating Center for multicenter studies, supporting clinical and pragmatic trials, and conducting research using large administrative and real-world datasets to evaluate healthcare outcomes and policy. As western Massachusetts’ only academic medical center and tertiary care provider, Baystate Health has a strong tradition of training healthcare professionals, delivering high-quality care, and advancing medical knowledge. We are committed to continuous learning, innovation, and inclusion—treating every patient, colleague, and community member with dignity and equity. Our organization actively works to increase diverse representation across all levels. Position Highlights: Serve as a key member of the Quantitative Methods Core, supporting and leading high-impact research. Independently consult with investigators on study design, analysis strategies, and interpretation of results. Conduct complex statistical analyses and contribute to grant proposals, manuscripts, and presentations. Work with large, real-world datasets, including electronic health records, claims, and registry data. Collaborate in a supportive, academically rich environment with a strong emphasis on equity and community engagement. A collaborative and self-motivated individual with strong statistical and communication skills, who thrives in a fast-paced research setting and is eager to support methodologically rigorous work from design to dissemination.
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Job Type
Full-time
Career Level
Mid Level