Bristol-Myers Squibb is a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases.
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Proteomic Data Analyst
We are seeking a highly motivated bioinformatician with academic and/or industry experience to join our Cambridge MA Translational Bioinformatics group. The successful candidate will work with bioinformaticians, experimental biologists and others to analyze large-scale datasets from high throughput proteomic profiling studies. You will provide expert guidance in how to process, normalize and analyze data from large-scale proteomic studies and collaborate with internal and external scientists to generate and test hypotheses about mechanisms of resistance to cancer therapies.
- Assess and integrate new methods and algorithms for proteomic data processing and analysis
- Collaborate with fellow bioinformaticians, statisticians, biologists, and clinicians to identify critical questions that may be addressed using proteomic datasets
- Analyze large-scale proteomic data sets to generate and test hypotheses about mechanisms of response and resistance in clinical and pre-clinical studies
- Provide expertise on issues related to normalization, imputation and analysis of LC-MS Proteomic data
- Present results and conclusions to internal and external stakeholders
- Ph.D. Computer Science, Computational Biology, Bioinformatics, Molecular Biology, Biochemistry, or related disciplines with 3+ years of relevant industry or academic experience
- Experience in oncology or immunology research is desirable
- Able to communicate and collaborate effectively with scientists across a broad range of disciplines
- Experience analyzing data and interpreting results from LC/MS proteomic profiling experiments
- Familiar with targeted and unbiased proteomic profiling workflows
- Familiar with standard proteomic data processing workflows such as MaxQuant, CHORUS and the CPTAC pipeline
- Experience in developing methods or new approaches for analyzing large-scale proteomic datasets is desirable
- Knowledge of R and relevant statistical methods are required.
- Familiarity with Linux/Unix and cloud computing are required.