Principal Bioinformatician​/Computational Biologist, Drug Safety

Help build the future of drug safety monitoring

Drug-induced toxicity remains one of the biggest causes of drug-development delay, clinical failure and patient harm. Current monitoring is often insensitive, organ-specific and reliant on damage becoming clinically apparent. We think there is a better way.

Tagomics is a Cambridge-based biotechnology company developing a next-generation multi-omic biomarker platform. Originating from pioneering research by Dr Robert Neely and colleagues at the University of Birmingham, our proprietary technology captures epigenetic features from circulating cell-free DNA that, when combined with advanced bioinformatics, let us detect and localise tissue injury across multiple organs, giving drug developers earlier and more biologically precise insight into treatment-related toxicity.

With successful proof-of-concept programmes behind us and pharmaceutical partners already engaged, we are building towards our first commercial products at Babraham Research Campus.

Read our recent scientific paper: (25)00223-1

This is a hands-on principal scientist role, not a management-only role. You will spend most of your time (around 70%) doing the science yourself: developing methods, writing code and working directly with the data. The rest of your time will be working with partners, team leadership and technical strategy.

You will be the most technically experienced bioinformatician on the drug safety programme. You will line-manage two senior bioinformaticians who are already strong practitioners, so we need someone with the depth to review their methods, support their technical development, and be someone they can bring difficult problems to.

You will also be key to our work with pharmaceutical partners. That means helping to design studies with them, explaining what the data can and can't tell them, and presenting results in a way that supports their decisions.

Do the science

• Develop, build and evaluate novel computational methods for epigenetic markers from cell-free DNA
• Adapt or write bioinformatics software alongside our software engineering team where existing tools fall short
• Drive toxicity biomarker discovery and refinement
• Translate findings into outputs partners can act on

Set the analytical direction

• Lead computational strategy independently, translating programme objectives into bioinformatics/analytical priorities
• Shape experimental designs (considering power/sample size, batch effects etc.), and plan appropriate controls
• Collaborate on long-term assay and product development

Work with partners

• Contribute to technical discussions with pharmaceutical and biotech partners, from study scoping through to results
• Present findings to partner scientists and decision-makers alongside our Programme Lead
• Help turn partner questions into well-designed studies and analyses

Lead and develop the team

• Lead, coach and technically challenge two senior bioinformaticians
• Set standards for methods, code quality and reproducibility across the programme
• Contribute to publications, IP and conference presentations

Essential

• PhD in bioinformatics, computational biology, or related fields, followed by substantial experience (8+ years) in industry or academia
• Current, hands-on coding expertise in Python and/or R. You should still be writing substantial analytical code in your current role
• In-depth knowledge of at least one omics technology
• A track record of developing new computational methods
• Experience applying statistical modelling or machine-learning methods to complex biological datasets
• Proven ability to set computational strategy and manage projects independently
• Experience developing the technical skills of other bioinformaticians
• Experience working with external partners, customers or collaborators in a commercial setting

Nice-to-have

• Python as your main language
• Knowledge of liquid biopsy, clinical studies or the drug development life cycle
• Previous line management experience
• Experience analysing epigenetic and/or transcriptomic datasets (e.g. MeDIP-seq, ATAC-seq, ChIP-seq, WGBS, RNA-seq)
• Knowledge of software engineering best practices: testing, code review, version control
• Experience in multi-disciplinary teams, including with laboratory members
• Startup, biotech or pharma experience

• You love solving problems and getting your hands dirty fixing them, alongside your team
• You can explain a complex result at the right level for your audience, from a five-minute summary for a…