We are seeking a highly talented and creative Computational Biologist. The ideal candidate will support our team’s ongoing target and drug discovery projects, identify and benchmark published tools as well as contribute to the development of novel in silico strategies. They will also design, implement, and apply statistical techniques to analyse and integrate large multi-omics datasets and work in close collaboration with biologists to interpret the analysis results.
They will be involved in projects related to the discovery and development of new cutting-edge therapies, primarily through the analysis and interpretation of NGS and other patient data sets to help identify and validate targets. This is a hands-on role working as part of a cross-functional team, interacting closely with biologists and bioinformaticians.
- Develop and implement statistical approaches to analyse diverse multi-omics data types (e.g. genomics, transcriptomics, proteomics) to help identify novel drug targets, biomarkers and therapeutic mechanisms
- Integrate and perform meta-analysis of public & internal data sets to generate testable hypotheses
- Develop and implement computational approaches to translate human genetic and genomic evidence to inform Oncology/Fibrosis target selection, validation and patient stratification decisions
- Support project teams and our external collaborators
- Find creative solutions and work with agility to address challenging scientific questions
- Work with bioinformaticians to implement innovative statistical analyses for a variety of research projects
- Work with bench scientists to help design experimental approaches and interpret data.
QUALIFICATIONS AND KEY SKILLS
- An MSc/PhD (or equivalent experience) in bioinformatics, computational biology, statistics or a related subject
- 3+ years of experience in biotech or pharma
- A strong understanding of statistical concepts and a demonstrated, high level proficiency in the application of a range of classical and modern statistical methods
- Experience with regression, including linear/non-linear models and mixed effect models, and a range of clustering methods such as graph-based approaches
- Experience in the statistical analysis and interpretation of diverse omics datasets
- Strong programming/scripting skills in a language such as R, Python
- Experience with databases such as TCGA, GTEx, cBioPortal and CCLE
- Data science skills to collect, integrate, mine and analyse complex biological data and translate them into testable hypotheses
- Strong communication, organisational and time management skills.
To apply for this position, please send a full CV along with a cover letter to firstname.lastname@example.org.