Manager, Biomarker Statistician wanted by Pfizer in Cambridge, MA.
Analyze clinical biomarker data including high dimensional data types such as lipidomics, proteomics, and transcriptomics data. Collaborate with Precision Medicine (PM) scientists and clinicians in the design, analysis and reporting of multi-dimensional biomarker data in early clinical trials. Define and evolve best practices across the group and partner with Research Units, Business Technology (BT) and partner line bioinformatics experts to align approaches, develop best practices, and assess and develop new technology.Ensure high-quality data analysis is incorporated into clinical exploratory-research programs in an objective fashion, facilitated by contribution of Exploratory Analysis Plans and summary of results, in partnership with other team members. Collaborate with research unit scientists in the design, analysis and reporting of genetics and 'omics analyses to enable decisions on target selection and prioritization. Partner closely with RUs, Clinical Teams, Statistics, BT and PM functions to identify, pilot and incorporate new bioinformatics tools to support clinical trials.
Requires: Ph.D. degree or foreign equivalent in statistics, computational biology, bioinformatics, or closely related field, plus 2 years of experience in the pharmaceutical industry in job offered or as Senior Research Statistician. Also requires 1 year of experience with/using the following (which may have been gained concurrently): working with high dimensional exploratory biomarker platforms (e.g. transcriptomics); applying statistical methods to the prospective design and analysis of scientific and/or clinical problems; SAS, R to perform big data analysis and data visualization; applying statistical analytical methods to genetics and omics data analysis; using early phase clinical study design principles, including power and sample size calculations; writing clinical biomarker SAPs; performing simulation, bootstrapping and permutation testing in a multidimensional modeling setting; analyzing linear mixed models; developing, influencing and championing biomarker analysis strategies with scientific team audiences and management.
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