At the core of GSK's R&D approach is the identification of new medicines by leveraging the vast amounts of human genetic and genomic data now being generated and applying cutting edge technologies in functional genomics and machine learning.
Within the Human Genetics team, we leverage major scientific and technological advances, including investment in biobanks linked to large-scale human health databases, cutting-edge informatics platforms and integration of biological pathway and functional genomics data to identify novel targets. We continue to evaluate these targets throughout their life in the pipeline, using genetics and genomics to identify the patients that are most likely to benefit and biomarkers to select these patients and assess efficacy.
The successful candidates will work in a multidisciplinary, collaborative and scientifically driven environment, interacting with GSK scientists and external collaborators to advance Oncology drug discovery and clinical development. This field-leading research addresses important drug discovery and development challenges, directly impacts GSK's R&D pipeline, and publishes results in top scientific journals.
Selected candidates will:
- Develop and implement computational approaches to translate human genetic and genomic evidence to inform Oncology target selection, validation and patient stratification decisions.
- Participate in project teams with GSK scientists and external collaborators.
- Find creative solutions and work with agility to address challenging scientific questions.
- Contribute to the developing science of drug target identification and validation, providing scientific expertise within Human Genetics and across the Research organization.
We are looking for professionals with these required skills to achieve our goals:
- PhD or equivalent experience in computational biology, bioinformatics, computational sciences, machine learning or biomedical engineering/biological sciences.
- Proficient in a programming language (such as R, Python or SQL).
- Experience with databases such as TCGA, GTEx and CCLE.
- Experience of analyzing and interpreting multi-omic data, especially RNA-Seq and single cell RNA-Seq, including data from patient samples.
- Data science skills to collect, integrate, mine and analyze complex biological data and translate them into testable hypotheses.
- Demonstrated skills working in teams to find creative ways to address scientific questions.
- Strong verbal and written communication skills and developing leadership skills for working with multidisciplinary teams and with international colleagues.
If you have the following characteristics, it would be a plus:
- Understanding of cancer and immuno-oncology biology (pathways, mechanisms, cell types).
- Application of pathway and network analysis methodology.
- Pharmaceutical or biotech industry experience working on Oncology or Immuno-Oncology related projects.