Head of Computational Biology

Barrington James is proud to be partnering with an emerging, AI-native drug discovery company. We are looking to appoint a Head of Computational Biology to lead the biological interpretation and application of our client's AI discovery platform.

The company is using AI, large-scale biological data and computational approaches to identify new therapeutic opportunities. The next stage is ensuring the platform is generating biologically meaningful outputs: credible targets, clear disease relevance, testable hypotheses and programmes that can move into experimental validation.

The company needs a senior computational biology leader who can sit between AI, data science, biology and discovery teams, helping turn complex datasets and model outputs into clear drug discovery decisions.

Key responsibilities:

• Lead computational biology strategy across AI-enabled target discovery and programme prioritisation
• Work with AI/ML and data science teams to interpret biological signals from large-scale datasets
• Help identify, validate and prioritise targets, pathways and disease hypotheses
• Translate platform outputs into clear biological rationale for internal discovery teams
• Build approaches around multi-omics, genetics, functional genomics, disease biology and patient-derived data
• Work closely with wet-lab biology, translational science and external partners to design validation strategies
• Establish decision-making frameworks for which AI-generated hypotheses should be progressed, tested or stopped
• Represent the computational biology strategy in senior internal, investor and pharma-partner discussions

Ideal background:

• Senior computational biology, bioinformatics, systems biology or AI-enabled discovery leadership experience
• Strong experience applying large-scale biological datasets to target discovery or therapeutic hypothesis generation
• Background working with omics, genetics, functional genomics, single-cell, patient datasets or disease biology platforms
• Able to work closely with machine learning, data engineering and experimental biology teams
• Track record turning computational insights into drug discovery programmes or validation strategies