Barrington James
Head of Computational Biology
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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.
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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
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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
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