Research Investigator, Early Stage Translational Bioinformatics Oncology

Bristol-Myers Squibb Company

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We are seeking a highly motivated bioinformatician with academic and/or industry experience to join our Integrated Translational Bioinformatics team with a focus on Cardiovascular/Fibrosis/Immunology team.  This bioinformatics group has a systems biology mandate to perform pathway, network and other meta-analysis of the relevant collection of internal and external omic data. The overriding goal is to address defined translational questions of fundamental importance to the fields of heart failure, fibrosis and immunological diseases such as defining patient populations, surrogate markers, drug repositioning and defining effective drug combinations in order to generate actionable scientific insights, hypotheses, and recommendations for stakeholders across the enterprise. The candidate is expected to be able to integrate multi-omic data from preclinical data or clinical trials as well as literature-based evidence to understand disease pathogenesis and progression, drug MOA and response to therapies. Team work skills will be essential to work effectively with biologists and statisticians to develop testable hypotheses from gene expression and other omic data to facilitate practical application of these molecular/genetic signatures. The candidate will internally face and interact with scientists within Discovery, Biomarker Science, Translational Biology, Clinical Development, Medical Affairs and Commercial organizations and will externally face and interact with academic investigators globally as a major scientific collaborator.

Responsibilities

  • Analysis of various –omic-scale data including RNA-Seq, Exome and Whole Genome Sequencing, single cell sequencing, and high throughput proteomics or metabolomics in order to nominate novel drug targets, enhance mode-of-action understanding, enable patient enrichment strategies and prioritize combination therapies.
  • Integration and mining of public data sets to further enhance our understanding of the factors influencing disease development.
  • Evaluate and analyze exploratory omics data from clinical trials to understand drug mechanisms, patient response and develop translational biomarkers. 
  • Extract insights and generate hypotheses to be evaluated in future trials and discovery experiments to inform our clinical strategies and discovery pipeline.
  • Collaborate with bioinformaticians, statisticians, biologists, and clinicians to identify critical questions that can be addressed via computational approaches. 
  • Define and implement statistical/computational analysis plans. 

Responsibilities

  • Analysis of various –omic-scale data including RNA-Seq, Exome and Whole Genome Sequencing, single cell sequencing, and high throughput proteomics or metabolomics in order to nominate novel drug targets, enhance mode-of-action understanding, enable patient enrichment strategies and prioritize combination therapies.
  • Integration and mining of public data sets to further enhance our understanding of the factors influencing disease development.
  • Evaluate and analyze exploratory omics data from clinical trials to understand drug mechanisms, patient response and develop translational biomarkers. 
  • Extract insights and generate hypotheses to be evaluated in future trials and discovery experiments to inform our clinical strategies and discovery pipeline.
  • Collaborate with bioinformaticians, statisticians, biologists, and clinicians to identify critical questions that can be addressed via computational approaches. 
  • Define and implement statistical/computational analysis plans. 

Qualifications:

  • Ph.D. in bioinformatics, engineering, statistics, physics, molecular biology, genetics, or a similar discipline with 2 years of industry or academic experience.
  • Strong background in -omic (RNA, DNA, epigenetic, proteomic) data analysis and biological interpretation.
  • Solid background in at least one biology area including fibrosis, heart failure and immunology.
  • The ability to work across organizations to define and solve problems that will benefit the whole. Capable of establishing strong working relationships across the organization.
  • Ability to communicate effectively with biologists, biostatisticians and computational scientists.
  • Proficiency using R and Bioconductor packages, at least one scripting language (e.g. Python, Perl), and database experience.
  • Experience working within a Linux high performance compute clusters and/or cloud-based computing platforms (Amazon EC2).
  • Working knowledge of publicly available biological databases including NCBI, Ensembl, dbGAP, GEO, SRA, etc.
  • Experience working with clinical trial data is a plus.

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Confirmed 6 hours ago. Posted 30+ days ago.

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