Biostatistician designing real-world evidence studies from EMR and claims data for Syneos Health. Applying causal inference, trial emulation, and statistical programming across healthcare projects.
Responsibilities
Design and execute real-world evidence studies using EMR and claims data
Conduct data specifications, statistical analysis plans, and protocols with key research objectives
Develop and apply robust statistical methodologies, including causal inference methods, GLM/GLMM, MMRM, survival analysis, and random forest
Apply trial emulation frameworks
Develop external control arms and borrowing strategies
Perform data analysis using healthcare coding systems such as ICD and NDC
Conduct sample size estimation and power calculations for observational and hybrid study designs
Collaborate with HEOR, Market Access, Regulatory, and Clinical Development stakeholders
Translate complex analytical results into clear, actionable insights through PowerPoint or study reports
Support methodological innovation in real-world evidence, including machine learning integration where appropriate
Requirements
M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field
≥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)
Programming expertise in at least one of: SAS, R, or Python
Working knowledge with SQL logic and OMOP data structures
Solid understanding of causal inference methods
Solid understanding of observational study design
Solid understanding of sample size and power considerations
Ability to independently write cohort definitions in SQL logic
Ability to debug data issues, including time zero alignment and exposure gaps
Understanding of concept mapping (ICD ↔ SNOMED ↔ RxNorm)
Ability to translate statistical estimands into censoring rules and data extraction logic
RWE CMH experience
Equivalent experience, skills, and/or education may be considered
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