Computational Biologist

Description

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.
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.


Qualifications

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 an 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.


Start date

September 03, 2018

How to Apply

Contact pankaj.agarwal@gsk.com while at ISMB otherwise apply on the website


Contact

Pankaj Agarwal
pankaj.agarwal@gsk.com; pankaj.agarwal@iscb.org