Real World Biostatistician - RWE CMH experience

Syneos Health•Púbol, CT

About The Position

Syneos Health is seeking a biostatistician with strong experience in real-world data (RWD) and observational study design, including safety studies and RWE CMH experience. This role will support evidence generation across multiple therapeutic areas. The focus will be on the design, analysis, and interpretation of observational studies using EMR and claims data to inform clinical development, HEOR, regulatory strategy, and market access.

Requirements

M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field.
≥5 years of experience in RWD/RWE 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, and sample size and power considerations.
Ability to independently write cohort definitions in SQL logic.
Ability to debug data issues (e.g., time zero alignment, exposure gaps).
Understanding of concept mapping (ICD ↔ SNOMED ↔ RxNorm).
Ability to translate statistical estimand → censoring rule and data extraction logic.
RWE CMH Experience.

Nice To Haves

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, and 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.
Build reusable cohort pipelines.
Optimize queries for large-scale databases.
Work across multiple CDMs (OMOP, Sentinel, PCORnet).

Responsibilities

Design and execute real-world evidence (RWE) studies using EMR and claims data.
Conduct 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, and 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, and 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.