Pfizer Sr. Scientist, Computational Biology in Cambridge, Massachusetts


As part of the Computational Sciences, apply computational and statistical analysis to interpret large-scale transcriptomics, functional genomics, proteomics, and human biology data for the advancement of small molecule and biologic discovery projects. The successful candidate will be responsible for performing integrative analysis relating multi-omics measurements with in vitro/in vivo/clinical biological endpoints and complex phenotypes and deliver testable hypotheses.


  • Perform computational analysis of high-dimensional datasets from model systems, with a focus on mechanism of action studies, mechanisms of resistance, and effective therapeutic combinations through the translation of bioinformatics data.

  • Deliver testable hypotheses/insights from complex multidimensional data to inform target/compound selection in high quality presentations & publications.

  • Identify and validate innovative approaches to improve quality and efficiency of hypothesis generation from experimental data

*Maintain awareness of emerging methods in computational biology and applications for novel omics technologies

  • Provide ad-hoc bioinformatics support to cross-disciplinary project teams

  • Serve as Computational Biology liaison to Medicinal Sciences project teams

  • Participate in analysis work groups and contribute innovative bioinformatics analysis to consortium-based projects


  • PhD in Bioinformatics, Biostatistics, Biological Sciences, relevant natural sciences required,

  • 2-3 years relevant experience applying quantitative approaches to solving biological problems, ideally in a pharmaceutical, biotech or comparable context.


• Strong background in human biology/medicine.

• Demonstrated experience in design and interpretation of in vivo and/or in vitro biological experiments.

• Demonstrated expertise in delivering insights/hypotheses from complex multi-dimensional biological data in a biomedical context.

*Demonstrated experience in handling NGS and other omics datasets

• Demonstrated experience applying computational and statistical approaches to deliver insights/hypotheses, e.g. multivariate and machine learning approaches.

• Demonstrated ability to effectively interface with biologists to communicate and discuss results, ideas, and follow-up experiments.

• Exceptional communication skills (oral and written) as demonstrated by publications & presentations.

  • Deep understanding of the statistical methods commonly used in omics analysis

EEO & Employment Eligibility

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