Description
As an Associate Director - Real World Biostatistics, you will lead the design and delivery of biostatistical methods and analyses for real-world data to support access and patient impact in the United States.
The role advises on and applies advanced biostatistical methods and AI tools to real world data (RWD) to generate real world evidence (RWE) for informing trial design, regulatory submissions, reimbursement and safety signals.
You will collaborate closely with diverse teams across medical affairs, epidemiology, health economics, market access, and external partners.
This role offers meaningful impact, strong career growth, and a chance to align with GSK’s mission of uniting science, technology, and talent to get ahead of disease together.
Responsibilities:
- Lead statistical design and analysis for real-world evidence studies using large healthcare data sets.
- Translate stakeholder questions into study protocols and statistical analysis plans that address bias and study limitations.
- Manage study execution, including timelines, budgets, quality control, and vendor coordination.
- Develop and review analytic code ensuring reproducibility and quality using programming tools such as R or SAS.
- Communicate results clearly to internal teams and external audiences, supporting publications and payer dossiers.
- Mentor junior biostatisticians and contribute to best practices across evidence generation teams.
Basic Qualifications:
- Master’s degree or PhD in biostatistics, epidemiology, statistics, data science, or related quantitative field.
- A minimum of 5 years of experience applying biostatistics to real-world data, pharmacoepidemiology, or health outcomes research.
Preferred Qualifications:
- PhD in biostatistics, epidemiology, or related discipline.
- Practical experience with programming languages such as R and/or SAS and reproducible analysis workflows.
- Experience working with large US real-world data sources such as insurance claims, electronic health records, or registries.
- Prior experience in the pharmaceutical or biotech industry supporting access, reimbursement, or regulatory decisions.
- A track record of peer-reviewed publications, conference presentations, or contributions to methodological guidelines.
- Familiarity with advanced causal inference methods, propensity score techniques, or longitudinal modeling.
- Strong knowledge of observational study design, causal inference methods, and methods to address bias.
- Experience mentoring and developing junior biostatisticians or early career scientists.
- Experience working in matrix environments with cross-functional teams and managing vendors.
- Excellent written and verbal communication skills, able to clearly explain technical results to non-technical audiences.
Benefits:
- Competitive salary
- Annual bonus based on company performance
- Healthcare and wellbeing programs
- Pension plan membership
- Shares and savings programme
- Remote or hybrid working model