Bioinformatics Investigator job in Cambridge
|Employer:|| Novartis Pharmaceuticals|
|Category:||Research and Development|
|Job Type:||Full Time|
Novartis Institutes for BioMedical Research, Inc. (NIBR) is seeking a computational biologist / bioinformatics analyst to join the Novartis Oncology Bioinformatics Team. Oncology Bioinformatics provides computational biology expertise to the department, from target ID through clinical development, for both targeted and immune therapies. We work at the cutting edge of science to solve important challenges in biomedical research, making use of both publicly available and internally generated unique and expansive preclinical and clinical datasets. The successful candidate will be highly motivated, creative, and an effective collaborator.|
Responsibilities will include:
• Working closely with wet- and dry-lab collaborators to analyze and interpret high-dimensional data such as pooled library NGS screening, single cell RNAseq, and proteomics.
• Supporting translational genomics, model characterization, and data mining efforts to solve unmet medical needs.
• Implementing and developing state-of-the-art computational methods and data mining strategies to address key challenges in oncology drug discovery (e.g. drug resistance, difficult-to-drug targets, harnessing anti-tumor immunity).
• Formulating testable hypotheses, and collaborating in the design of rigorous experiments.• M.S. or Ph.D. in computational biology, statistics, computer science, or a related field
• Familiar with fundamental concepts in molecular biology, statistics, and bioinformatics
• Fluency in one or more programming languages with bioinformatics applications (e.g. Python or R)
• Experience with statistical methods for mining 'omics data (genomics, epigenetics, proteomics) and/or NGS data strongly preferred
• Knowledge of cancer genomics, immunology / immune-oncology, clinical and translational science preferred
• Experience with current analysis methods for processing NGS data, high throughput pooled screening, or other high-dimension data sets preferred