Epidemiologist- Forward

About Intuitive

Intuitive started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later that question still fuels everything we do. As a global leader in robotic-assisted surgery and minimally invasive care, our technologies—like the da Vinci surgical system and Ion—have transformed how care is delivered for millions of patients worldwide. We are a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human.

Staff Epidemiologist, Real‑World Evidence Generation

$154,400 – $222,200 per year

Stanford, CA

Design and execute data-driven analyses using claims databases and real‑world evidence to demonstrate the clinical and economic value of emerging robotic platforms. This role focuses on rigorous statistical analysis of U.S. commercial and Medicare claims data, real-world outcomes research, and translating findings into evidence that supports regulatory submissions, payer discussions, and clinical adoption. The successful candidate will combine epidemiologic expertise with advanced statistical methods to generate peer‑reviewed publications and evidence dossiers that demonstrate procedure value and patient benefit.

• Real‑World Evidence Generation & Claims Data Analysis:
  Design and execute comprehensive analyses using U.S. commercial claims databases (e.g., Market Scan, Optum), Medicare data, and other population-level observational datasets to evaluate procedure outcomes, safety, and resource utilization.
• Statistical Analysis & Methodology:
  Perform advanced statistical analyses including logistic regression, survival analysis, propensity score matching, instrumental variable analysis, and other causal inference methods appropriate for observational data. Develop and document standardized analytical code libraries (SAS, R, SQL) that enable reproducible, transparent research.
• Health Economics Integration:
  Support health economic analyses by providing clinical outcome data, cost drivers, and utilization patterns derived from claims databases. Partner with HEOR colleagues to integrate real‑world evidence with economic models, quantify resource utilization, cost burden, and clinical benefits for target patient populations, and contribute epidemiologic and statistical expertise to develop value propositions grounded in evidence.
• Evidence Translation, Publication Strategy & External Scientific Engagement:
  Lead peer‑reviewed publication development for real‑world evidence studies. Translate research findings into clear, evidence‑based value dossiers and briefing documents for regulatory, payer, and clinical stakeholders. Communicate findings through publications, regulatory submissions, and scientific presentations; collaborate with cross‑functional teams; identify and engage key opinion leaders; develop publication roadmaps; and lead strategic positioning of findings at conferences and clinical societies.
• Cross‑Functional
  
  Collaboration:
  
  Work with Clinical Affairs, Regulatory, and Market Access teams to identify key evidence gaps and prioritize analyses that support product value demonstration. Partner with R&D and Product Management to inform design considerations and clinical endpoints. Mentor junior analysts; engage external stakeholders to validate findings and ensure clinical relevance.

• Advanced degree in Epidemiology, Public Health, Biostatistics, Statistics, or related quantitative field (PhD, MSc strongly preferred); MD/DO with substantial epidemiologic or clinical research background may be considered.
• 7–10+ years of experience in epidemiologic research, real‑world evidence generation, or outcomes research, with demonstrated expertise in claims database analysis.
• Advanced proficiency in statistical programming languages (SAS, R, SQL) and experience building and managing large, complex datasets from multiple sources.
• Direct experience accessing, analyzing, and interpreting U.S. commercial and Medicare claims databases (e.g., Market Scan, Optum, CMS data).
• Strong knowledge of epidemiologic study designs, causal inference…