ROLE SUMMARY
Standard biological experiments now easily generate enormous volumes of data which require specialized and sophisticated approaches in order to extract meaningful biological insights.
The Bioinformatician will be accountable to and will work closely with biologists, computational biologists, and human geneticists across the Internal Medicine Research Unit developing, implementing and applying 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 to address key drug discovery questions on biological and disease mechanisms, biological rationale, target safety and patient stratification.
The Bioinformatician will 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 Bioinformatician position offers an opportunity to execute science-based drug discovery within one of the world’s leading developers of human therapeutics, at the Pfizer’s Kendall Square research facility in Cambridge Massachusetts.
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, including Type 2 Diabetes, 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, DNAseq, etc), and other ‘Omics data types.
Lead internal/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, Computer Science, Applied Mathematics, or related field required; 5+ years relevant experience applying quantitative approaches to solving biological problems, preferably in a pharmaceutical, biotech or comparable context; 3+ years experience in evaluating data relevant to cardiovascular and metabolic diseases, including but not limited to, Type 2 Diabetes, Non-alcoholic Fatty Liver Disease / Non-alcoholic Steatohepatitis, and Heart Failure.
PREFERRED QUALIFICATIONS
Extensive experience in bio-computational programming, scripting, querying or statistical analysis languages such as R, python, perl, SQL, as well as Linux OS for high performance computing. Extensive knowledge and experience using bioinformatics libraries such as Galaxy, Bioconductor, Ingenuity Pathways, SciPy, etc. Experience using application programming interfaces is encouraged.
Demonstrated experience applying computational approaches to multi-dimensional datasets to deliver insights and hypotheses, e.g., multivariate, Bayesian and machine learning approaches.
In depth knowledge of relevant public and proprietary databases, methods and tools.
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.
Pharmaceutically relevant experience or formal training in computational biology, bioinformatics, computer science or medicine.
Demonstrated ability for sound experimental design for in-silico experimentation/workflows required, in addition to ability to effectively interface with biologists to communicate/discuss results, hypotheses, and follow-up experiments.
PHYSICAL/MENTAL REQUIREMENTS
Primarily sitting at a desk and moving through offices. Light travel load. No extra-ordinary requirements.