Real World Biostatistician - RWE CMH experience

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Real World Biostatistician - RWE CMH experience based in the United States.

This role offers the opportunity to contribute to high-impact real-world evidence generation that directly informs clinical development, regulatory strategy, and market access decisions across multiple therapeutic areas. You will work at the intersection of biostatistics, epidemiology, and healthcare data science, leveraging large-scale real-world datasets such as EMR and claims data. The position focuses on designing and executing observational studies that support evidence-based decision-making in life sciences. You will apply advanced statistical methodologies, including causal inference and trial emulation, to answer complex research questions. The role involves close collaboration with cross-functional teams including HEOR, regulatory, clinical, and market access stakeholders. This is a highly analytical and collaborative environment where scientific rigor, innovation, and clear communication are essential to success.

• Design, develop, and execute real-world evidence (RWE) studies using EMR and claims data to support clinical, regulatory, and market access objectives.
• Develop study protocols, statistical analysis plans (SAPs), and data specifications aligned with research goals and stakeholder needs.
• Apply advanced statistical methods including causal inference techniques (propensity score methods, weighting, matching), survival analysis, GLM/GLMM, and trial emulation frameworks.
• Conduct observational and hybrid study analyses, including external control arm development and evidence borrowing strategies.
• Perform data extraction and analysis using healthcare coding systems such as ICD and NDC, ensuring data integrity and accuracy.
• Conduct sample size estimation and power calculations for complex observational study designs.
• Collaborate with cross-functional stakeholders across HEOR, clinical development, regulatory, and market access teams.
• Translate complex statistical findings into clear, actionable insights for scientific, clinical, and business audiences.
• Support methodological innovation in RWE, including exploration of machine learning applications in real-world data contexts.
• Contribute to study reporting, presentations, and regulatory documentation to support decision-making.

Requirements

• Master’s or Ph.D. in Biostatistics, Statistics, Epidemiology, or a related quantitative field.
• 5+ years of experience in real-world data (RWD) / real-world evidence (RWE) analytics in industry or equivalent settings.
• Strong hands-on experience working with EMR and/or claims databases.
• Proficiency in programming languages such as SAS, R, or Python.
• Solid understanding of observational study design and causal inference methodologies.
• Experience with healthcare coding systems such as ICD, NDC, and related clinical data standards.
• Working knowledge of SQL and familiarity with data models such as OMOP.
• Experience performing cohort definition, data debugging, and alignment of exposure/outcome definitions in real-world datasets.
• Strong statistical foundation including survival analysis, sampling methods, and power calculations.
• Ability to translate estimands into analytical frameworks and data extraction logic.
• Strong communication skills with the ability to present complex findings to diverse stakeholders.
• Experience in therapeutic areas such as cardiovascular, diabetes, or metabolic disorders is a plus.
• Exposure to trial emulation, hybrid designs, or external control arms is highly desirable.

Benefits

• Competitive compensation package aligned with industry standards.
• Fully remote opportunity.
• Comprehensive health, dental, and vision insurance coverage.
• Opportunity to work on high-impact real-world evidence projects influencing healthcare decisions globally.
• Exposure to advanced methodologies in biostatistics, epidemiology, and data science.
• Collaborative, science-driven environment with cross-functional global teams.
• Strong focus on professional development and methodological innovation.
• Participation in complex, large-scale studies across multiple therapeutic areas.