Research Investigator, Bioinformatics

Job Location
270/280 Albany Street
Cambridge, MA 02142

Job Description

Research Investigator Bioinformatics

We seek an experienced bioinformatics scientist / computational biologist to join a dynamic Translational Medicine bioinformatics group dedicated to the development of cancer therapeutics

  • Proactively engage with project teams to define crisp scientific questions that can be addressed with an appropriately powered experimental designs
  • Supply any computational, statistical, machine learning or modeling capability needed to synthesize from this design into an actionable recommendation relative to the teams question
  • Identify targets and biological contexts (e.g., T or B-cell repertoire, immune phenotypes, copy number, mutational, or expression signatures) in which therapies are likely to be beneficial by mining immune, genetic, genomic or screening data
  • Manage and develop group of two or  more associates and/ or contractors
  • Identify from a patient and model system databases the epidemiology of these biological contexts and targets
  • In collaboration with project teams propose, the  best analysis strategies for target discovery, using methods based for instance on differential expression, co-regulation, correlated amplification, recurrent alterations, etc.; in so doing, also help devise statistical metrics for deciding significance, and ranking schemes for integrating data from many sources.
  • Develop Translational Medicine plans (e.g., patient selection and indication biomarkers) in collaboration with Translational and Experimental Medicine project teams



Job Related Experience

  • A minimum of 5-years research (academia or industry) experience
  • Ph.D. in a computational, statistical, biophysics, or bioinformatics related fields
  • First-hand multi-parametric data mining experience for target identification and biomarker discovery. For example, multivariate analysis; dimensionality reduction methods; parametric and non-parametric statistical methods; Bayesian statistics; pattern recognition or classification methods.
  • First-hand experience at integrating publicly or commercially available genetic, genomic, and immune datasets with novel experimental data to identify testable hypotheses
  • Statistical programming skills like SAS/S-plus, R/bioconductor and pipeline pilot or equivalents
  • Excellent understanding of immunology and hands-on research experience investigating complex lymphoid and/or myeloid cell subsets
  • A background in Immune Oncology, Translational Research, Pathway Analysis, Quantitative Methods for Systems Biology, Network Analysis, or Simulation are a plus.



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