Description Real World Biostatistician - RWE CMH experience
Job ResponsibilitiesJob Description
Biostatistician - Real-World Evidence (RWE) Role Overview We are seeking a biostatistician with strong experience in real-world data (RWD) with observational study design and safety study experience in addition to RWE CMH experience. This role will support evidence generation across multiple therapeutic areas. This role will focus 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 (RWE) 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:
- 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 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
- Observational study design
- Sample size and power considerations
- Some examples: Independently write cohort definitions in SQL logic; Debug data issues e.g., time zero alignment, exposure gaps; Understand concept mapping (ICD ↔ SNOMED ↔ RxNorm); Translate statistical estimand → censoring rule and data extraction logic
- RWE CMH Experience
Preferred Qualifications - Experience in one or more therapeutic areas:
- 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
- Advanced (nice-to-have, not always required)
- Build reusable cohort pipelines
- Optimize queries for large-scale databases
- Work across multiple CDMs (OMOP, Sentinel, PCORnet)
Core Competencies - Analytical rigor and methodological depth
- Cross-functional collaboration
- Ability to operate with agility across diverse projects and therapeutic areas
- Clear and effective scientific communication
Additional InformationTasks, duties, and responsibilities as listed in this job description are not exhaustive. The Company, at its sole discretion and with no prior notice, may assign other tasks, duties, and job responsibilities. Equivalent experience, skills, and/or education will also be considered so qualifications of incumbents may differ from those listed in the Job Description. The Company, at its sole discretion, will determine what constitutes as equivalent to the qualifications described above. Further, nothing contained herein should be construed to create an employment contract. Occasionally, required skills/experiences for jobs are expressed in brief terms. Any language contained herein is intended to fully comply with all obligations imposed by the legislation of each country in which it operates, including the implementation of the EU Equality Directive, in relation to the recruitment and employment of its employees.
