Statistics Leader Advanced Biostatistics

GSK   •  

Providence, RI

Industry: Pharmaceuticals & Biotech


Not Specified years

Posted 38 days ago

Advanced Biostatistics and Data Analytics Statistics Leader

The Advanced Biostatistics & Data Analytics (ABDA) Centre of Excellence is part of the Biostatistics department in the R&D Development organisation. The primary focus of ABDA is to drive methodology development and software solutions to influence trial design, analysis and quantitative decision-making strategies within clinical development. The group also has a wide-ranging remit to work with all parts of the GSK business, providing the opportunity to influence statistical thinking across the spectrum of drug development, from early discovery, to registration and marketed product support.

Key Responsibilities include, but are not limited to:

Join a group of innovative statistical scientists working in close collaboration with project teams and key stakeholders to design more effective studies which: raise the likelihood of success (where the drug is safe and effective), increase the likelihood of an early termination (where the drug is ineffective or unsafe), and/or reduce the expected cost of the trial or program.

Critical job elements include:

  • Being a strong collaborator: while being an individual contributor, this role needs to provide leadership combined with subject-matter expertise capable of driving strategy and influencing leaders within the organization.
  • Driving systematic use of quantitative decision-making methods to support development plans across all stages of the drug development lifecycle
  • Developing and implementing:
  • Innovative designs for biomarker-related studies to enable objective decision-making in early clinical development
  • Designs and methods that make systematic use of prior information (including expert knowledge) and/or adaptive designs
  • Methods for analysis and visualization of data from wearable/digital devices, and identifying suitable clinical endpoints based on these data
  • Methods for handling treatment discontinuation and withdrawal from clinical trials (estimands)
  • Communication of complex statistical designs/concepts to non-statistical audiences in an efficient manner
  • Providing statistical consultancy and training to statistical and non-statistical staff as needed
  • Creating and fostering collaborations with external statistical groups (both industry and academia) in order to promote statistical innovation within GSK and across the pharmaceutical industry
  • Proactively contributing to development and implementation a communication strategy to rapidly advance awareness and uptake of new statistical methods and novel designs within GSK

Why You?

Basic Qualifications

  • PhD (or equivalent) in statistics/biostatistics or related quantitative discipline and at least 3 years' experience in drug development or other relevant industry or academic setting
  • Demonstrable evidence of statistical innovation and technical statistical strength, including publication in major scientific journals
  • Experience of using Bayesian methods
  • Competent user of modern biostatistical methods relevant to drug development, such as mixed modelling, longitudinal/time series modelling, survival analysis, clinical trial simulation
  • Thorough working knowledge of current statistical software packages (at least one of SAS, R, Stata)
  • Experience of statistical programming (e.g. in R, SAS, Python, Java)
  • Demonstrated ability to effectively communicate complex statistical ideas to non-statisticians
  • Self-motivated, independent worker
  • Record of building and maintaining effective working relationships

Preferred Qualifications

  • Experience in one or more areas of statistical research activity relevant to pharmaceutical industry (e.g. use of historical data / informative priors, adaptive designs, missing data, benefit-risk modelling, evidence synthesis). Evidence of deeper expertise in at least one of these areas would be beneficial but not essential
  • Experience of using MCMC software packages (e.g. BUGS, JAGS, Stan, INLA, SAS Proc MCMC)
  • Experience of parallel/distributed computing
  • Experience with machine learning algorithms, multivariate statistical analysis techniques and/or biomarker modelling in drug development
  • Proven ability to apply innovative statistical thinking to meet project and/or business objectives. Demonstrable evidence of using innovative statistical thinking to influence practice would be beneficial but not essential.
  • A good understanding of drug development processes and strategies would be beneficial, but not essential