Job Description

ROLE SUMMARY

Responsible for conducting statistical analyses primarily on large scale, high-dimensional human genetics datasets, such as GWAS and exome sequencing dataset, and for integrating these results with public datasets and biological knowledge databases in order to make inferences informing therapeutic targeting strategies and patient selection. Additional responsibilities will include analysis of clinical outcomes by pre-defined genetic markers. The candidate will ultimately be responsible for ensuring sound statistical thinking and methods are utilized for the design, analysis, and statistical interpretation of such studies, focused on genetics and 'omics datasets utilized by therapeutic areas across Pfizer’s R&D unit.

ROLE RESPONSIBILITIES

Collaborate with various scientists in the design, analysis and reporting of genetics and ‘omics data, including statistical interpretation of published or internally generated results in order to inform decision-making and identify new targets.

Core member of scientific teams responsible for determining strategy and delivering results in a timely and high-quality environment.

Interact with integrative biology leads, pharmacogenomics leads, and external experts to assure sound quantitative approaches are applied to data collection and analysis.

Provide mentoring and guidance to clinical statisticians on aspects of pharmacogenomics in clinical trials.

Ensure rigorous approaches are taken to ensure good scientific practices are followed: excellent statistical methods utilized and documented in protocols, analysis plans and manuscripts, and assay methods are appropriately blinded, randomized and designed to meet clear objectives.

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

Integrate genetics across public resources in order to make inferences related to portfolio prioritization and new target identification.

Use statistical expertise to influence both internal and external reports and presentations, with the aim to improve the quality of internal decision making and increase our influence on the external environment.

QUALIFICATIONS

PhD in Statistics or related computational field, or demonstration of sufficient statistical training to be successful in the position including experience or academic training in statistical genetics

At least one full year experience applying statistical methods to scientific and/or clinical problems. Pharmaceutical or biotech applications desired.

Demonstrated skill manipulating large datasets and automating such tasks.

Demonstrated understanding of GWAS and sequence-based analysis.

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.

Experience with multivariate statistics and statistical methods to quantify uncertainty in multi-dimensional data, and with QC procedures for GWAS and sequencing data.

Demonstrated strong communication skills, both oral and written.

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

Must be able to explain statistical techniques and considerations to non-statisticians

TECHNICAL SKILL REQUIREMENTS

Must have R or SAS programming skills and familiarity with a Linux/UNIX environment

Knowledge or proficiency in PLINK, ability to use genetic imputation software such as MACH, IMPUTE, Minimac, or imputation servers such a UMich or TopMed, and awareness of the latest techniques in sequence-based analysis and other genetics tools will be extremely helpful