Founding Computational Biologist

• Design and build the first scientific and operational workflows for an AI platform supporting pharma R&D, working closely with the AI engineering team. Examples include interpreting toxicology and DMPK data, study design, reviewing CRO data and drafting study reports.
• Define how scientific quality is measured: build the biology test sets and set the standard for what a reliable, traceable result looks like. Work with the AI team to create expert-labelled data that improves the models.
• Analyse data from early pharma partners and present the findings directly to their scientists. Lead hands-on, in-person work with partner research teams.
• Work with the product team to turn scientists’ needs into clear product requirements.
• Help grow the team by playing a central role in hiring 2-3 junior computational biologists.

The person

• A PhD in computational biology, bioinformatics, pharmacology or a similar discipline, with 3-8 years’ experience in pharma or biotech R&D.
• Broad experience across the drug development pipeline, from early target work through to IND/CTA. Preclinical development is the current emphasis (toxicology, DMPK, safety pharmacology, PK/PD), but wider pipeline experience is valued.
• A solid understanding of what goes into an IND-enabling package and how preclinical results inform progression decisions.
• Strong Python and R skills, comfortable using AI-assisted coding tools, and has delivered at least one tool or platform that other scientists have used regularly.
• Experience building or critically evaluating LLM or agent-based tools for biology would be a bonus.
• Comfortable presenting results to pharma project teams.

Less suited to: ML researchers, chemistry-focused ML specialists, people who have worked in only one data type (e.g. only single-cell or only genomics), or people who are purely managers.