• BS plus 2 years relevant experience or, MSc, or PhD in computational biology, bioinformatics, biology, or a related field.
• Experience with analysis and processing of genomic data (e.g., RNA-Seq, ChIP-Seq, exome-Seq, and microarray-based gene expression).
• Knowledge and experience utilizing commercially available pathway and network analysis tools/databases especially those utilized in the field of cancer research (e.g., GSEA, DAVID).
• Ability to communicate in a clear and concise manner in both written and verbal form.
• Work effectively in a multidisciplinary team setting.
• Adept at managing multiple priorities in an efficient and organized manner.
• A publication record that demonstrates ability to successfully utilize bioinformatic approaches.
• A scientific background and interest in cancer biology and/or cancer genetics.
• Demonstrated ability to integrate multiple data types.
• Direct knowledge of the drug discovery and/or development process.
• Machine learning and scripting skills.
• Experience analyzing DNA methylation datasets.
• Training in statistical analyses.
The Cancer Epigenetics Discovery Performance Unit (DPU) within Oncology R&D is seeking a highly motivated computational biologist to join a multidisciplinary team working to discover and develop novel epigenetic medicines for the treatment of cancer. The successful candidate will be responsible for providing key computational biology support to the unit by integrating and mining public and propriety genomic data, investigating pathways/networks to identify predictive and pharmacodynamic biomarkers, and understanding the cellular mechanism of lead molecules in both the clinical and pre-clinical setting. This exceptionally talented individual will be adept at working with large genomic data sets, possess a broad knowledge of genetic and genomic technologies used in cancer research, and have an understanding of cancer biology. You will enjoy working in a dynamic, fast-paced environment, and be comfortable communicating complex bioinformatic approaches and analyses to a diverse group of scientists.
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