Your Responsibilities:
The Computational Biology Department supports Drug Discovery and Development at GSK Pharmaceuticals R&D through the integrative analysis of internal and external biomedical data. Our analysis competencies are quite diverse and include target identification and validation, genetics, pathways and networks, disease indications, text mining, molecular evolution, gene expression, microbiome/host pathogen analysis, drug repositioning and machine learning. We contribute to multiple phases of the drug development pipeline and our work results in developing new medicines for important diseases of unmet medical need.
Your primary role as a Computational Biologist will be to identify, develop, implement and use computational biology methods to impact drug discovery and development. Applicants should have a strong background in bioinformatics, computational biology, computer science, machine learning, artificial intelligence, and/or genomics, combined with knowledge of biology. You should be comfortable working in a dynamic and highly collaborative environment, and you will be expected to communicate complex informatics principles, methods, analyses and results to scientists from diverse backgrounds. Furthermore, you will be encouraged to develop and advance your computational biology skills as well as your internal and external scientific profile through presentations and peer-reviewed publications.
GSK offers excellent work-life balance for its employees and is an equal opportunity employer.
Why GSK?:
Today there are still millions of people without access to basic healthcare, thousands of diseases without adequate treatments and millions more people who suffer from everyday ailments. At GSK we want to change this.
We want to help people do more, feel better, live longer
We have three world-leading businesses that research, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products.
Across the US, we employ more than 15,000 people who strive to help others do more, feel better and live longer. This work starts one person, one community, and one story at a time.
As a science-led, global healthcare business with clear values, we offer countless opportunities for people at various stages of their careers. On-the-job learning and rewarding individual contributions are extremely important to us. We believe in creating an inclusive and safe working environment and supporting our employees to help their communities.
Some examples of recent publications by GSK Computational Biology:
1. Khaladkar M, Koscielny G, Hasan S, Agarwal P, Dunham I, Rajpal D, Sanseau P. Uncovering novel repositioning opportunities using the Open Targets platform. Drug Discov Today. 2017 Dec;22(12):1800-1807.
2. Reisdorf WC, Chhugani N, Sanseau P, Agarwal P. Harnessing public domain data to discover and validate therapeutic targets. Expert Opin Drug Discov. 2017 Jul;12(7):687-693.
3. Hurle MR, Nelson MR, Agarwal P, Cardon LR. Trial watch: Impact of genetically supported target selection on R&D productivity. Nat Rev Drug Discov. 2016: 15 (9), 596-7.
4. Koscielny G et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 2017 Jan 4;45(D1):D985-D994.
5. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, Floratos A, Sham PC, Li MJ, Wang J, Cardon LR, Whittaker JC, Sanseau P. The support of human genetic evidence for approved drug indications. Nat Genet. 2015 Aug;47(8):856-60.
6. Arat S, Spivak A, Van Horn S, Thomas E, Traini C, Sathe G, Livi GP, Ingraham K, Jones L, Aubart K, Holmes DJ, Naderer O, Brown JR. 2015. Microbiome changes in healthy volunteers treated with GSK1322322, a novel antibiotic targeting bacterial peptide deformylase. Antimicrobial Agents and Chemotherapy. 59:1182-1192.
7. Rajpal DK, Klein JL, Mayhew D, Boucheron J, Spivak AT, Kumar V, Ingraham K, Paulik M, Chen L, Van Horn S, Thomas E, Sathe G, Livi GP, Holmes DJ, Brown JR. 2015. Selective Spectrum Antibiotic Modulation of the Gut Microbiome in Obesity and Diabetes Rodent Models. PLoS One. 2015 Dec 28;10(12):e0145499.
8. Cheng J, Yang L, Kumar V, Agarwal, P. Systematic evaluation of connectivity map for disease indications Genome Medicine 2014; 6:95
9. Qu X, Freundenberg J, Sanseau, P, Rajpal D. Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study. Drug Disc Today 2014; 19. 1364-1371
10. Rajpal DK, Qu XA, Freudenberg JM, Kumar VD. 2014. Mining emerging biomedical literature for understanding disease associations in drug discovery. Methods Mol Biol. 2014;1159:171-206.
11. Smith SB, Magid-Slav M, Brown JR. 2013. Host response to respiratory bacterial pathogens as identified by integrated analysis of human geneexpression data. PLoS One. 2013 Sep 27;8(9):e75607
12. Agarwal P, Sanseau P, Cardon LR. 2013. Novelty in the target landscape of the pharmaceutical industry. Nat Rev Drug Discov. 2013 Aug;12(8):575-6.
13. Freudenberg JM, Rajpal N, Way JM, Magid-Slav M, Rajpal DK. 2013: Gastrointestinal weight-loss surgery: glimpses at the molecular level. DrugDisc Today. 2013 Jul;18(13-14):625-36.
14. Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. 2013. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013 Apr; 93(4):335-41.
15. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, Mooser V. 2012. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012 Apr 10;30(4):317-20.
16. Qu XA, Rajpal DK. 2012. Applications of Connectivity Map in drug discovery and development. Drug Discov Today. 2012 Dec;17(23-24):1289-98.
Basic qualifications:
• PhD in Computational biology, bioinformatics, computational sciences, machine learning, artificial intelligence, or biomedical/biological sciences.
• Experience in a programming language (such as R, Python or Perl) for complex data analysis.
Preferred qualifications:
• Knowledge of genome-wide genetic, genomic, pathway and network methods.
• Skills to collect, integrate, mine and analyze complex biological data and translate them into testable hypotheses.
• Knowledge of and experience with common bioinformatics databases, resources and tools.
• Knowledge of the drug discovery and development process.
• Demonstrated experience in processing multiple large scale genomic and genetic platforms, such as transcriptomic, proteomic or Next Generation sequencing data, including understanding of pathway/network analyses.
• Statistical analysis skills
• Strong written and oral communication skills.
• Ability to work effectively in multidisciplinary teams.
• Publication record in peer reviewed journals.
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