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Position: Biostatistics Manager
Institution: Pfizer Inc.
Location: Cambridge, Massachusetts, United States
Duties: Collaborate with clinical teams and scientists in the design, analysis and reporting of clinical studies incorporating digital health technologies; Work with scientists to understand the biology and improve existing or derive new digital endpoints and develop ‘fit-for-purpose’ statistical models; Collaborate with interdisciplinary teams including clinicians, data scientists and data managers to develop, validate and deploy algorithms and analysis pipelines to derive digital endpoints for use in clinical trials
Requirements: MSc in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Electrical Engineering coupled with high level of statistical expertise. Research experience with mathematical/statistical modeling using complex data and three or more years of industrial or similar experience; Fluency in R programming; Strong background in experimental design and statistical analysis including good understanding of inference and probability, competence in contemporary linear and predictive modeling including (longitudinal) mixed models, nonlinear regression, and predictive modeling
   
Text: Biostatistics Manager Collaborate with clinical teams and scientists in the design, analysis and reporting of clinical studies incorporating digital health technologies; Work with scientists to understand the biology and improve existing or derive new digital endpoints and develop ‘fit-for-purpose’ statistical models; Collaborate with interdisciplinary teams including clinicians, data scientists and data managers to develop, validate and deploy algorithms and analysis pipelines to derive digital endpoints for use in clinical trials MSc in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Electrical Engineering coupled with high level of statistical expertise. Research experience with mathematical/statistical modeling using complex data and three or more years of industrial or similar experience; Fluency in R programming; Strong background in experimental design and statistical analysis including good understanding of inference and probability, competence in contemporary linear and predictive modeling including (longitudinal) mixed models, nonlinear regression, and predictive modeling
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