february, 2021

01feb1:00 am28(feb 28)1:00 amPfizer Senior Computational Biologist

Event Details

ROLE SUMMARY

Standard biological experiments now easily generate enormous volumes of data. Understanding the biological message underlying this data requires a computationally-savvy biologist applying specialized and sophisticated approaches to extract meaningful biological insights.

The Senior Scientist will be accountable to and will work closely with lab-based biologists, computational biologists, and human geneticists within the Internal Medicine Research Unit to address key drug discovery questions on biological and disease mechanisms, biological rationale, target safety and patient stratification through the use and development of computational tools and methods to store, process, analyze, visualize and integrate in-house and public genetics, epigenetics, RNAseq, NGS, metabolomics, proteomics and other ‘omics data types.

The Senior Scientist will also work with other computational and statistics colleagues across the Integrative Biology groups within the Worldwide Research, Development, and Medical organization and colleagues in Research Business Technologies to leverage a broader suite of computational capabilities and analytical tools, and lead the evaluation of cutting-edge tools, to elucidate target and disease mechanisms, develop and test therapeutic hypotheses, identify disease biomarkers and understand patient stratification.

The Senior Scientist position offers an opportunity to execute science-based drug discovery within one of the world’s leading developers of human therapeutics, at Pfizer’s Kendall Square research facility in Cambridge Massachusetts.

ROLE RESPONSIBILITIES

  • Lead internal and external efforts to provide actionable biological insights and build scientific understanding of specific drug targets and biological pathways within the context of cardiovascular and metabolic diseases, primarily in Cachexia but also in Type 2 Diabetes, Obesity, Non-alcoholic Fatty Liver Disease, and Heart Failure through the development, implementation, and application of tools and methods to analyze genetic, NGS (including but not limited to RNAseq, scRNAseq, etc), and other ‘Omics data types.
  • Lead internal and external collaborations that secure access to novel or proprietary data types and innovative computational methodologies to advance our ability to process and analyze novel biological data types and understand disease pathophysiology from large-scale molecular and phenotypic data.

 

BASIC QUALIFICATIONS 

  • Ph.D. in Biological Sciences, Bioinformatics, Molecular Biology, Biochemistry, Computer Science, Applied Mathematics, or related field required; 5+ years relevant experience applying quantitative approaches to addressing biological questions, preferably in a pharmaceutical, biotech or comparable context;
  • 3+ years experience in analyzing data relevant to cardiovascular and metabolic diseases preferred, including but not limited to, Cachexia, Obesity, Adiposity, Type 2 Diabetes, Non-alcoholic Fatty Liver Disease / Non-alcoholic Steatohepatitis, and Heart Failure.
  • Strong curiosity about human biology and disease pathology.

PREFERRED QUALIFICATIONS

  • Demonstrated ability for sound experimental design for in-silico experimentation/workflows required, in addition to the ability to effectively interface with biologists to communicate/discuss results, hypotheses, and follow-up experiments.
  • Demonstrated experience in design, execution, and interpretation of in vivo and/or in vitro biological experiments generating large-scale molecular datasets, especially RNAseq, and other NGS data-types.
  • In-depth knowledge of relevant public and proprietary databases, methods and tools. Good understanding of the statistical methods used in omics analyses.
  • Extensive experience in bio-computational statistical programming languages (R) and working knowledge of scripting languages (Python preferred) is essential.
  • Familiarity with database management (SQL) a plus.
  • Familiarity with high-performance computing is essential and cloud computing preferred.
  • Experience with relevant packages from Bioconductor (preferred) or SciPy is essential.
  • Experience with creating data visualization tools such as Shiny apps is a plus. Experience using application programming interfaces (APIs) also a plus.
  • Experience applying computational approaches to multi-dimensional datasets to deliver insights and hypotheses, e.g., multivariate, Bayesian, and machine learning approaches a plus.

 

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Time

1 (Monday) 1:00 am - 28 (Sunday) 1:00 am

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