Real-World Biostatistician - RWD/CMH Expert

Syneos Health, Inc.; Canada; Hybrid; USD 110,000 - 170,000; Full time

Job summary

Syneos Health is seeking a Real World Biostatistician with strong RWD experience to design, analyze, and interpret observational studies using EMR and claims data across therapeutic areas. This role supports clinical development, HEOR, regulatory strategy, and market access.

Apply advanced statistical methods such as causal inference, propensity scores, trial emulation, and external control borrowing, while collaborating with HEOR, Regulatory, and Clinical teams to produce actionable insights

Qualifications

• M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field.
• 5 years of experience in RWA/RWD analytics (industry or equivalent).
• Proficiency with EMR and/or claims data.
• Programming in SAS, R, or Python; SQL knowledge.
• Experience with ICD, NDC coding systems and OMOP data structures.
• Strong communication and cross-functional collaboration skills.

Responsibilities

• Design and execute real-world evidence studies using EMR and claims data.
• Develop SAPs and protocols aligned with research objectives.
• Apply robust statistical methods including causal inference and trial emulation.
• Perform sample size estimation and power calculations.
• Collaborate across HEOR, Market Access, Regulatory, and Clinical Development.
• Translate results into decision-ready insights for stakeholders.

Skills

Causal inference methods

Observational study design

Power calculations

Data analysis communication

EMR data handling

NDC/ICD coding

Education

M.S. in Biostatistics or related field

Ph.D. in Biostatistics or related field

Tools

SAS

R

Python

SQL

OMOP data model

Job description

Syneos Health is seeking a Real World Biostatistician with strong RWD experience to design, analyze, and interpret observational studies using EMR and claims data across therapeutic areas. This role supports clinical development, HEOR, regulatory strategy, and market access.

Apply advanced statistical methods such as causal inference, propensity scores, trial emulation, and external control borrowing, while collaborating with HEOR, Regulatory, and Clinical teams to produce actionable insights