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Bioinformatician

We are seeking a highly motivated bioinformatician to join our team in Integrative Genomics and Technologies at Bristol-Myers Squibb. The successful candidate will help advance Bristol-Myers Squibb’s Leads Discovery and Optimization pipelines through the strategic application of cutting-edge bioinformatics approaches. The candidate will be primarily working with large scale kinase inhibition assays to optimize compound screening assays, but may also contribute to other ongoing projects.

Responsibilities
• Analyze large scale kinome screening assays to identify kinase inhibition profiles of in-house compounds. 
• Mine large-scale external data sets to further our understanding of kinase inhibition mechanisms.
• Integrate multiple datasets (including transcriptomic, proteomic, and chemical) to identify potential drug candidates and investigate molecular pathways affected by the compounds for potential off target effects. 
• Use machine learning algorithms to develop analysis pipelines to inform chemists and biologists of chemical structures that may lead to improved kinase inhibition. 
• Collaborate with bioinformaticians, statisticians, biologists, and chemists to design experiments to further investigate potential drug candidates and optimize future experiments. 
• Contribute to other ongoing projects by analyzing RNA sequencing data, mining of public datasets (e.g. CMAP, TCGA, etc.), and/or creating visualizations of data using R-Shiny, Spotfire, or other applications. 

Qualifications
• Recent Ph.D. in bioinformatics, engineering, statistics, physics, molecular biology, genetics, or a similar discipline.
• Proven track record of independent research under minimal supervision.
• Experience working with large scale datasets. 
• Strong background in data science for mining databases to analyze and interpret biological and/or chemical data.  
• Ability to communicate effectively with biologists, biostatisticians and computational scientists.
• Basic background in molecular biology is preferred. 
• Experience with machine learning is preferred. 
• Proficiency using R and Bioconductor packages is preferred.