Senior Computer Biologist

2 weeks ago

England, United Kingdom Ochre Bio Full time

Summary We are looking for a talented and highly motivated (Senior) Computational Biologist to join Ochre Bio’s In Silico Biology team on an exciting journey to support the development of advanced RNA medicines for one of the most pressing healthcare challenges of our time.
The role will support discovery and validation data analysis within a functional genomics and imaging team, with a sharp focus on generating novel target recommendations, validating them in models (including actual human livers), and translating them into RNA therapeutics for chronic liver disease.
This role will be based at Oxford Science Park. UK remote work is possible, with site visits.

Be a key part of the ‘discovery’ squad within the In Silico Biology Team, and drive the next phase of Ochre’s discovery programme.
Develop and contribute to approaches for interrogating NGS data, including single-cell RNA-seq data, spatial transcriptomics data, bulk RNA-seq data to evaluate effects of gene perturbation on cell models
Involvement in experimental design & modelling of deep phenotyping or omics data in close collaboration with wet-lab scientists. Work as a member of cross-functional teams bringing together best-in-class Drug Discovery, Drug Development, Product & Project Management capabilities.
Routinely use Python/R for data analyses, in particular data wrangling, modelling, and visualisation. Develop solutions to increase automation, reliability and scalability of our data processing pipelines and workflows.
Maintain progress in personal and professional development to better domain, technical, and professional skills.
Present research updates to project teams and to the management team.
Contribute to training initiatives to foster a data-driven culture

PhD in Computational Biology / Computer Science / Machine Learning / Statistics with relevant experience in transcriptomics analysis.
Experience with experimental design, exploratory data analysis, reporting and visualisation of data.
Experience with single cell transcriptomics analysis including data QC, data integration, cell type annotation, differential expression/abundance and pathway analysis.

Experience with regulatory genomics (for instance lncRNAs)
Experience in building and deploying pipelines (e.g. Nextflow) for data science

Deep understanding of human biology beyond the statistical/ML models being developed
Good knowledge with rapidly prototyping, writing and maintaining Python and/or R code, harnessing statistical tools for biological interpretations.

Familiarity with cloud computing (AWS)
Familiarity with Perturb-Seq analyses
Familiarity with spatial transcriptomic analyses
Familiarity with project management tools (Smartsheet/ ClickUp / JIRA)

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