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Senior Scientist: Data Science/Bioinformatics
Zoetis is seeking a skilled and experienced bioinformatician/data scientist to contribute towards efforts in VMRD Genetics. The successful candidate will be part of multi-disciplinary team working with diverse models and biological systems to enhance development of Zoetis’ portfolio. The individual will be instrumental in helping teams across the organization to design, analyze and interpret complex data. The scientist will keep up to date with the latest developments in next generation technologies, applications of sequencing, incorporation of large and diverse data into health solutions.
- Key applications involve identification of disease targets, drug safety, immune responses, and animal health.
- The successful candidate must have a solid background in both prokaryotic (bacteria and viruses) and eukaryotic biology.
- The desired candidate should have experience in next generation sequencing (NGS) analysis, data interpretation and integration of other “omics” type data (proteomics and metabolomics).
- An ideal candidate would ideally like to work in the host-pathogen interaction realm with a systems biology perspective.
- The candidate should have excellent communication skills and the ability to work with dynamic teams of scientists.
EDUCATION AND EXPERIENCE
Candidates with Ph.D. in Cell biology, Microbiology, Virology, Biochemistry, Genomics, Bioinformatics or other related fields are preferred. Post-doctoral experience with analyzing, and interpreting -omics data is required.
- Candidates should be proficient in at least one programming language (such as Python, BASH, C, C++, R)
- Experience with systems biology approaches to elucidate complex processes required
- The candidate should have experience with:
- analyzing DNA, RNA, and protein sequences (DNA-Seq, RNA-Seq, Proteomics etc.)
- pathway analysis workflows and data integration
- microbiome and metagenomics analysis
- variant detection and CNV analysis
- Experience working in a high-performance computing (HPC) environment and managing large and complex biological data is necessary
- Experience with high density SNP data, such as genome wide association studies (GWAS) and genomic selection is a plus