Description
Syneos Health is seeking a biostatistician with strong experience in real-world data (RWD) with observational study design and safety study experience in addition to RWA CMH experience.
The role will support 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 real-world evidence (RWA) studies using EMR and claims data; Conducting data specs, SAP and protocol with key research objectives
- Develop and apply robust statistical methodologies, including:
- Causal inference methods (e.g., propensity score methods, weighting, matching; GLM or GLMM, MMRM; survival analysis; random forest)
- Trial emulation frameworks
- External control arm development and borrowing strategies
- Perform data analysis using healthcare coding systems (e.g., ICD, NDC)
- Conduct sample size estimation and power calculations for observational and hybrid study designs
- Collaborate cross-functionally with stakeholders across: HEOR, Market Access, Regulatory, Clinical Development
- Translate complex analytical results into clear, actionable insights, e.g. PowerPoint or study report for decision-making
- Support methodological innovation in RWE, including integration of machine learning approaches where appropriate
Required Qualifications
- 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, observational study design, sample size and power considerations
Preferred Qualifications
- Ph.D. strongly preferred
- Experience in one or more therapeutic areas: Diabetes, Cardiovascular disease, Metabolic disorders
- Familiarity with: trial emulation methodologies, external control borrowing / hybrid designs, basic machine learning methods applied to RWD
- Demonstrated ability to work across multiple therapeutic areas (TAs) in a fast-paced environment
- Strong communication and stakeholder engagement skills