Senior Manager, Clinical RWE Strategist, Epidemiology at Boston Scientific in Arden Hills, MN

  • Company: Boston Scientific
  • Location: Arden Hills, MN
  • Job type: full time
  • Workplace: onsite
  • Posted: 2026-10-30

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Job description

Additional Location(s): US-MA-Marlborough; US-MN-Arden Hills Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions. About the role: Boston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the Top 100 list, reflecting the culture our employees experience every day. The Senior Manager, Clinical Real-World Evidence (RWE) Strategist, Epidemiology serves as a scientific and methodological expert, leading the strategic use of real-world data (RWD) and evidence generation across Boston Scientific. This individual serves as a clinical epidemiology and observational research expert, partnering with Clinical, Regulatory, R&D, Medical Affairs, Marketing, HEMA, HEOR, and divisional stakeholders to translate important clinical and business questions into scientifically rigorous real-world evidence programs. This role requires deep expertise in clinical epidemiology, study design, and biostatistical principles to support the development of fit-for-purpose evidence strategies across the product lifecycle. The successful candidate will work closely with physicians, clinical scientists, biostatisticians, and regulatory professionals to design and execute observational studies using claims, electronic health records, registries, and other real-world data sources.Particular emphasis will be placed on developing scientifically rigorous study protocols, defining clinically meaningful endpoints and target populations, evaluating potential sources of bias and confounding, and ensuring that study designs generate credible evidence to support regulatory, clinical, reimbursement, and commercial decision-making. The incumbent will contribute to establishing RWE as a core scientific capability of Boston Scientific, supporting device development, post-market evidence generation, indication expansions, labeling strategies, quality initiatives, and market differentiation. Work model, sponsorship, relocation: This position may be based in Marlborough, Massachusetts, or Arden Hills, Minnesota. At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our local office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time. Your responsibilities will include: Serve as the clinical epidemiology and observational research lead for enterprise RWE initiatives, providing methodological expertise in study design, protocol development, endpoint selection, cohort definition, and interpretation of findings. Collaborate with physicians, Clinical Affairs, Medical Affairs, Regulatory Affairs, and Biostatistics to transform clinical questions into scientifically rigorous and fit-for-purpose RWE research studies. Lead the design of observational studies using claims, electronic health records, registries, device-generated data, and other real-world data sources, including cohort studies, comparative effectiveness studies, natural history studies, safety studies, and external control arms. Develop and review study concepts, protocols, statistical analysis plans, and publication strategies while ensuring methodological rigor and alignment with scientific and regulatory expectations. Provide expertise on epidemiologic methods, bias assessment, confounding control, missing data, causal inference, and study validity to support high-quality evidence generation. Guide the identification and val

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