Real World Biostatistician (RWE Cardiometabolic) - Remote

About the role

Syneos Health is hiring a Real World Biostatistician focused on cardiometabolic real-world evidence (RWE), open to candidates based in Canada and the US. The role supports evidence generation across multiple therapeutic areas, focusing on the design, analysis and interpretation of observational studies using EMR and claims data to inform clinical development, HEOR, regulatory strategy and market access.

Key responsibilities

• Design and execute RWE studies using EMR and claims data
• Write data specifications, SAPs and protocols aligned with key research objectives
• Apply causal inference methods such as propensity scores, weighting and matching, GLM/GLMM, MMRM, survival analysis and random forests
• Develop trial emulation frameworks and external control arm development and borrowing strategies
• Analyze data using healthcare coding systems (e.g. ICD, NDC)
• Perform sample size estimation and power calculations for observational and hybrid study designs
• Collaborate with HEOR, Market Access, Regulatory and Clinical Development teams
• Turn analytical results into clear insights, such as PowerPoint presentations or study reports
• Support methodological innovation in RWE, including machine learning approaches where appropriate

Required profile

• M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology or a related field (Ph.D. strongly preferred)
• At least 5 years of experience in RWA/RWD analytics (industry or equivalent)
• Strong experience with EMR and/or claims data
• Proficiency in healthcare coding systems (e.g. ICD, NDC)
• Preferred: experience in diabetes, cardiovascular disease or metabolic disorders, and familiarity with trial emulation, external control borrowing and basic machine learning methods

Required skills

• Programming in at least one of SAS, R or Python
• Working knowledge of SQL logic and OMOP data structures
• Understanding of causal inference, observational study design and sample size/power
• Preferred: building reusable cohort pipelines, optimizing queries for large databases, and working across OMOP, Sentinel and PCORnet