Pfizer Postdoctoral Research Fellow, Statistical Genetics in Cambridge, Massachusetts

Responsible for developing/implementing novel statistical methodologies for analyzing and drawing inferences from genetic data, and for quantitatively integrating internal data with results from literature in order to inform decisions on target prioritization and identify patient subpopulation.

Responsibilities

  • Lead to develop and apply new statistical methods integrating multiple data sources, e.g. Electronic Health Record data, self-reported data, clinical endpoints and biomarker data.

  • Develop novel multivariate statistical methods to incorporate correlations among multiple datatypes; explore the genetic associations of disease progressions; investigate the association of loss-of-function genetic variants with EHR in large-scale sequencing datasets; and use Bayesian techniques to characterize the distributions of true genetic effect sizes in GWAS

  • Integrate genetics/omics across clinical trials in order to make inferences related to personalized medicine, using appropriate meta-analysis techniques.

  • Present novel scientific findings at both internal and external meetings and publish them in peer-reviewed journals in order to support internal decision making on drug targets and precision medicine efforts and to increase influence on the external environment.

Qualifications

  • Recent PhD (0-3 years) in Statistical Genetics or related field such as Applied Mathematics, Statistics, Physics or Population Genetics/Genomics, with demonstrated experience analyzing genetic data.

  • Management and analysis of large genetic studies and sequence-based analysis.

  • Proficiency in running simulations and mixed models.

  • Familiarity/expertise in meta-analysis and potential issues surrounding combining results from different studies, both from a design perspective and in terms of subject ascertainment.

  • Knowledge of modern statistical methods of utilizing summary statistics.

  • Understanding of the value of Bayesian methods to scientific research.

  • Demonstrated strong communication skills, both oral and written.

  • Demonstrated ability to work effectively as a part of a team.

Technical Skill Requirements

  • Must have R and Python/Perl/Shell programming skills and familiarity with a Linux/UNIX environment, experience with C/C++ will be helpful.

  • Knowledge of GWAS statistical genetics software (PLINK/MaCH/SHAPIT/IMPUTE2/etc.), and awareness of the latest techniques in sequence-based analysis and other genetics tools will be a plus.

EEO & Employment Eligibility

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