The rapid growth of high-dimensional datasets at all levels of granularity together with broader availability of powerful computational methods offer new opportunities for science-based drug discovery. The Inflammation & Immunology research unit is capitalizing on this emerging science with an embedded computational group working in close collaboration with experimentalists.
The Inflammation and Immunity Research Area seeks an experienced computational biologist / data analyst to join us in the fight against autoimmune disease. The Computational Biologist position offers an opportunity to execute science-based drug discovery within one of the world’s leading developers of human therapeutics, at the Pfizer Biomedical Institute based in the Cambridge Innovation Hub.
Work closely with colleagues within the Systems Immunology function as well as project teams and to inform the I&I drug discovery pipeline based on integration and analysis of omics scale datasets, e.g. RNASeq, genetics, FACS/cyTOF, single cell RNAseq, proteomics, and cell proportions to understand disease biology, potential drug targets and specific genetic variants.
Collaborate with experimental biologists and others to design, process and analyze single cell RNAseq and related experiments to support target identification and portfolio projects.
Using knowledge of the immune system and computational skills to internalize (or design) and apply fit-for-purpose computational approaches to understand immune dysfunction in patients and work towards hypotheses for therapeutic mechanisms.
Support the identification and integration of all internal and external disease and treatment related data to support novel targets. Apply and/or develop analytically methods to interpret multiple data sets of from human genetics studies, internal clinical studies and electronic medical records.
Identify targets, biomarkers and biological contexts (e.g., RNA expression signatures, immune phenotypes, protein signatures, T or B-cell repertoire or mutational signatures) in which therapies are likely to be beneficial by mining immune, genetic, genomic or screening data.
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EEO & Employment Eligibility
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Ph.D. in Computational Biology, Biological Sciences, Bioinformatics, Computer Science, Applied Mathematics or other natural science required;
3+ years relevant experience applying quantitative approaches to solving biological problems, preferably in a pharmaceutical, biotech or comparable context.
Proficiency in programming, scripting, querying or statistical analysis languages such as R, python, perl, unix
Strong interest in biology and immunological diseases.
Research experience in any of the following areas: human biology/medicine, immunology, autoimmunity, fibrosis.
Demonstrated expertise in delivering insights and hypotheses from complex multi-dimensional biological data in a biomedical context.
Solid foundation in the fundamentals of statistics with a demonstrated experience in designing and applying computational approaches to deliver insights and hypotheses, e.g., multivariate, Bayesian and modern machine learning approaches.
Experience with reproducible workflow approaches such as Jupyter Notebooks or R Markdown.
Demonstrated experience in design, execution and interpretation of in vivo and/or in vitro biological experiments, especially RNAseq or other NGS and FACS/cyToF.
Pharmaceutically relevant experience or formal training in computational biology, bioinformatics, computer science or medicine.
In depth knowledge of relevant public and proprietary databases, methods and tools.
Excellent communication skills (oral and written) as demonstrated by publications & presentations with the ability to work in a highly collaborative environment.