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About the Role
The AI Accelerator aims to deepen the understanding of disease biology and increase the probability of discovering and developing innovative medicines by applying state-of-the-art AI capabilities. The AI Reasoning team focuses on causality and rationalizing model outputs to transform them into trusted scientific hypotheses.
Responsibilities
- Translate strategy into a scientific roadmap, priorities, and success criteria for evidence-grounded AI, and drive delivery against them.
- Strengthen AI-generated hypotheses by combining model findings with supporting and contradictory evidence, provenance, and biological context.
- Lead the development of evaluation and benchmarking approaches, defining credibility and traceability of evidence.
- Partner with computational biology and human genetics to ensure work meets disease-biology and target-evaluation needs.
- Provide senior matrix leadership and scientific judgment to inform build, buy, and partner decisions.
- Mentor scientists across teams and contribute to Accelerator-wide priorities.
- Build external credibility in AI-assisted scientific reasoning and evidence synthesis.
- Use state-of-the-art large language models, AI assistants and agents, curated knowledge bases and evidence-retrieval capabilities to explain and rationalize model outputs.
- Provide the interpretability layer that builds trust in model-generated hypotheses.
- Report to the Director, AI Reasoning, and sit on the AI Reasoning Leadership Team.
- Contribute to AI Reasoning strategy.
- Act as an experienced matrix leader who influences through multidisciplinary squads.
- Set scientific standards and evaluation criteria.
- Represent the domain in cross-organizational fora.
- Serve as the AI Accelerator's senior scientific bridge to Computational Innovation’s portfolio-facing teams.
Requirements
- PhD in Computational Biology, Bioinformatics, Machine Learning, or a closely related field.
- Industry experience in pharma, techbio, or AI-native drug discovery organizations.
- Understanding of target identification, patient stratification, biomarker discovery, translational biology, and how scientific evidence informs portfolio decision-making.
- Experience using modern AI tools for scientific work, such as large language models, scientific AI assistants and agents, retrieval-augmented reasoning, curated knowledge bases, ontologies, pathway resources, or biomedical knowledge graphs.
- Track record of setting direction across teams without formal authority.
- Mentoring senior AI scientists and shaping the priorities of adjacent teams.
- Excellent written and verbal communication skills.
- Ability to translate between AI and biology audiences.
- Ability to represent the Accelerator credibly in senior scientific and business fora.
- Sufficient understanding of AI model development to engage deeply with AI Scientists and Research Engineers on design and evaluation trade-offs.
- Experience assessing build/buy/co-develop options for scientific AI capabilities.
- Experience building scientific work packages with external partners.
- Familiarity with regulatory expectations around AI-derived evidence in life sciences.
Skills
- Causality
- Rationalizing model outputs
- Large language models
- AI assistants
- Agents
- Curated knowledge bases
- Evidence-retrieval
- Interpretability
- Scientific roadmap development
- Evidence synthesis
- AI-assisted scientific reasoning
- Retrieval-augmented reasoning
- Ontologies
- Pathway resources
- Biomedical knowledge graphs
- AI model development
- Build/buy/co-develop assessment
- Regulatory expectations for AI-derived evidence
Location
- London
Work Type
- Hybrid
- Full-time
Experience Level
- Senior Staff
- Experienced matrix leader
Education Level
- PhD
About the Company
- Boehringer Ingelheim has been recognised as a Top Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.