Director, Biostatistics
Job in
Collegeville, Montgomery County, Pennsylvania, 19426, USA
Listed on 2026-01-12
Listing for:
GSK
Full Time
position Listed on 2026-01-12
Job specializations:
-
IT/Tech
Data Scientist, Data Security, Data Analyst, Data Science Manager
Job Description & How to Apply Below
Director, Real-World Biostatistics
Company: GSK
Locations: USA – Pennsylvania – Upper Providence, Durham Blackwell Street; UK – London – New Oxford Street; USA – Massachusetts – Waltham
The Director, Real-World Biostatistics is a key role focused on advancing strategy and methodology in the entire course of drug/vaccine development using real-world data (RWD) within the enterprise. This position requires providing deep biostatistical expertise, strategic insight, and supporting methodological innovation to enhance the development and commercialization of pharmaceutical products, primarily in the oncology research unit (RU).
Key Responsibilities- Lead and oversee the execution of real-world studies, ensuring methodological rigor, quality control, and regulatory adherence.
- Create and refine statistical analysis plans, conduct complex statistical analyses, and convey findings to internal and external stakeholders.
- Apply fit-for-purpose non-interventional statistical methods tailored to specific study objectives, ensuring robust data interpretation and insight generation.
- Understand RU/assets to apply appropriate tools (e.g., variable definitions, code lists) and data sources, and leverage hands‑on RWD expertise to guide the selection and appropriate use of complex health data sets, including experience authoring technical specification documents.
- Develop in-depth knowledge on the assigned asset(s) and act as senior‑level RWD consultant on matrix teams.
- Mentor junior staff, guiding and developing their capabilities through mentorship, training, and professional growth opportunities while promoting knowledge sharing, continuous learning, and an innovative and collaborative environment.
- Lead statistical efforts for assigned projects, manage timelines, resources, quality control, and coordinate across departments (e.g., epidemiology, health economics and outcomes research, clinical).
- Engage in strategic communication within the organization and with external audiences presenting statistical analyses and insights clearly and effectively at conferences, in publications, and during key stakeholder meetings, reinforcing the value of biostatistical contributions.
- Stay informed on industry trends, incorporate emerging biostatistical methods to enhance study designs and analytics, and participate in methodological research for the development of analytical techniques.
- Provide biostatistical expertise on RWD during regulatory submissions, meeting preparations, and addressing queries to ensure alignment with regulatory standards while offering statistical guidance for organizational decision‑making processes.
- Stay informed with guidance documents from regulators to industry on use of RWD for regulatory decision‑making.
- Ph.D. in Biostatistics, Statistics, Epidemiology or related disciplines with 8+ years (or Masters plus 10+ years) of working within the pharmaceutical/biotech industry (preferably in real‑world evidence, epidemiology, or health outcomes functional areas).
- Experience working with drug development processes and strategies, utilizing innovative statistical skills to meet project and/or business objectives.
- Experience leading pharmacoepidemiology and/or health outcomes analytics using RWD (e.g., electronic health record; insurance claims; registries) and applying observational study design and biostatistical principles to clinical/epidemiological research.
- Experience in programming languages (e.g., R, Python) and applied experience with observational data.
- Experience in working according to regulatory requirements pertaining to RWD and clinical trials.
- Experience in managing projects, delivering results in matrixed environments.
- Experience in methodological research with contributions to publications in real‑world data analytics.
- Experience in causal inference methodology such as propensity score based approaches, doubly‑robust estimations including target maximum likelihood estimation (TMLE),…
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