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Senior Biostatistics Director

2 months ago

London, Greater London, United Kingdom GlaxoSmithKline Full time
About the Role

We are seeking a highly skilled and experienced Senior Biostatistics Director to join our team at GlaxoSmithKline. As a key member of our Biostatistics and Data Science function, you will play a critical role in shaping the future of our disease understanding and driving innovation in statistical methodology.

Key Responsibilities
  • Enhance our disease understanding through the implementation of advanced statistical and/or machine learning methodologies across a range of areas from complex in-vitro models through to market access activities involving real world data to directly support business critical projects.
  • Identify opportunities to utilize statistical methods to further enhance our disease understanding. Provide strategic leadership for cross functional quantitative groups to help determine the optimal analytical methods using appropriate data sources.
  • Support the upskilling of Statisticians and other quantitative disciplines in statistical methodologies for disease understanding including their direct application in clinical development planning.
  • Lead planning and implementation of moderate-to-high complexity projects involving cross-functional teams, ensuring they have clear objectives, achievable timelines, intermediate milestones, and well-defined criteria for success.
  • Present statistical principles, methods, study designs, and/or results transparently and precisely to GSK researchers and management across all levels, ensuring appropriate understanding and contributing to better quantitative decision-making across drug development.
  • Create connections and interact with external scientific groups (industry, academia, regulators) and vendors through presentations, publications, and collaborations, in order to contribute to innovation and uptake of novel statistical methodology and tools within GSK and across the pharmaceutical industry.
Requirements
  • PhD in Statistics, Biostatistics, or a closely related discipline (with formal statistical training).
  • Significant post-graduate experience in clinical trials research in the pharmaceutical or a related industry, and evidence of leading statistical methodology innovation. Individuals with an MS (or equivalent) in Statistics, Biostatistics, or a closely related discipline (with formal statistical training) may be considered provided they substantial post-graduate experience.
  • Evidence, through publications, of methodological innovation and technical strength in advanced statistical modelling and/or modern machine learning techniques with consistent and substantial contributions occurring beyond research conducted during graduate studies.
  • Proficiency in coding languages such as R, SAS, Python or other more foundational languages (e.g., C++).
  • Proficient in both spoken and written English, with the capability to clearly communicate both new and established statistical methods and results based on those methods to peers without substantial statistical training.
Preferred Qualifications
  • Substantial post graduate work experience in statistical consulting, working alongside researchers from various fields to devise practical solutions in response to other researchers' scientific questions of interest.
  • Substantial post graduate work experience applying a broad array of foundational statistical methodologies, including but not limited to methods for study/experimental design, linear models, generalized linear models, linear and generalized mixed models, survival analysis, Bayesian methods, longitudinal data analysis, and causal inference.
  • Substantial post graduate work experience applying quantitative methodology in at least two of the following key areas: complex innovative trial design (e.g., Bayesian adaptive design), mathematical and/or computational models for disease processes, analysis of high-dimensional data (e.g., 'omics data, imaging data).
  • Knowledge of or interest in multiple aspects of drug development, including drug discovery, preclinical development, and clinical development.
  • Experience working in high performance computing environments, including running intensive computational methods or handling large simulation experiments, as well as implementing machine learning operations (MLOps) on cloud platforms.
  • Evidence of ability to publish quantitative methodological research or scientific research findings using advanced quantitative methodologies with consistent and substantial contributions occurring beyond research conducted during graduate studies.
  • Demonstrable evidence of influencing and partnering with diverse stakeholders of varying levels of seniority.