Clinical Bioinformatician

About the role

To support generation of insights for clinical assets through understanding of mode of action and treatment effects as seen in patient-level molecular data.

What you’ll do

• With minimal guidance of senior staff, processes, analyzes and interprets data from omics studies for assets in the clinical portfolio, to refine understanding of biological mechanisms, disease pathobiology, characterise treatment effect, safety signals and other relevant insights.
• Develops approaches, methods, analysis pipelines utilizing state-of-the-art tools and technologies to enable the above.
• Documents code, analyses and ensure reproducibility of results according to best practices suited to patient-level data
• Draft reports detailing methods and results that require minimal revision.  Drafts / contributes to scientific publications that present the results of these analyses (conference abstracts/presentations and manuscripts).

Requirements

• Master/PhD with at least 5 years of experience, preferably with industry experience.
• Expertise in pre-processing, integration and analysis of different omics data types, in particular extensive experience analysing large bulk and single-cell RNAseq datasets from complex studies, and experience deriving relevant biological insights from these. Experience with spatial data, epigenomics and proteomics is an advantage.
• Expertise in the development and application of methods pertinent for functional interpretation of omics data e.g. pathway enrichment, functional module detection, causal reasoning.
• Experience analyzing and deriving insights using large omics datasets from clinical trials and relating omics data results to clinical variables is a plus.
• Excellent computational skills, such as experience of operating in a HPC Unix environment, cloud-based computational and/or pipelining platforms, and containerization technologies (e.g., Docker) for reproducible and portable bioinformatics workflows. Experience using DNAnexus is a plus.
• Proficiency using R in particular, and also other programming languages common to the bioinformatics community. Ability to document code and follow coding best practices, in particular suited to patient-level data, to ensure reproducibility of results.
• Good understanding of statistics, data mining, and visualization of biological data types and the standard tools used in these domains.
• Ability to interpret and communicate efficiently (orally and with reports) the outcomes of analytical work to both expert and non-expert scientific audience to foster efficient decision-making. Preferably experience in authoring scientific publications.