Computational Biologist - Leeds, United Kingdom - Ochre Bio

Description

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.

Key Responsibilities

• 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.
• Lead, co-develop, and run pipelines in a cloud computing environment.
• Maintain progress in personal and professional development to better domain, technical, and professional skills.
• Contribute to documenting processes and code in the form of SOPs. Present research updates to project teams and to the management team.
• Contribute to training initiatives to foster a data-driven culture

Qualifications & Experience

Required

• 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.

Desirable

• Experience working with large single cell datasets (atlas level).
• Experience with regulatory genomics (for instance lncRNAs)
• Experience in building and deploying pipelines (e.g. Nextflow) for data science

Skills & Competencies

Required

• 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.

Desirable

• Familiarity with Github.
• Familiarity with cloud computing (AWS)
• Familiarity with FAIR principles and how they can be applied in modern drug discovery
• Familiarity with Perturb-Seq analyses
• Familiarity with spatial transcriptomic analyses
• Familiarity with project management tools (Smartsheet/ ClickUp / JIRA)

Cultural & Working Norms

The role requires behaviours that encourage thriving in the face of failures, summarised as Ochre's three laws.

Clarke's Law:

"Any sufficiently advanced technology is indistinguishable from magic" (Don't fear failure, be ambitious)

Murphy's Law:

"What can go wrong, will go wrong." (You will fail, learn quickly)

Wheaton's Law:

"Don't be a d*ck" (Others will fail you, support them)