Agentic AI Lead – Disease Biology & Target Discovery
Career Category
ResearchJob Description
Position Overview
The GCF6 Agentic AI Lead - Disease Biology & Target Discovery is a senior scientific and technical leader responsible for developing AI-enabled approaches that accelerate disease understanding, target identification, mechanism-of-action analysis, and translational research.
This role combines expertise in disease biology and biomedical research with knowledge of modern AI technologies, including knowledge graphs, foundation models, retrieval systems, and agentic AI architectures.
The leader works closely with scientists and ML engineers to design intelligent workflows that integrate biological knowledge, data, literature, and computational models to support decision-making across the discovery process.
This role serves as the primary scientific lead for AI applications in disease biology and target discovery.
Core Responsibilities
Scientific AI Strategy
Develop and execute a roadmap for AI-enabled capabilities supporting:
- Disease biology research
- Target identification and prioritization
- Mechanistic biology
- Biomarker discovery
- Literature synthesis
- Evidence generation
- Translational science workflows
Identify opportunities where AI can improve scientific reasoning, evidence integration, and discovery productivity.
Knowledge-Driven AI Systems
Lead development of AI solutions that leverage:
- Knowledge graphs
- Biomedical ontologies
- Scientific literature
- Internal research data
- External biological databases
Define approaches for integrating structured and unstructured knowledge into AI-assisted scientific workflows.
Agentic Workflow Design
Design intelligent workflows that combine:
- Knowledge retrieval
- Scientific reasoning
- Evidence synthesis
- Hypothesis generation
- Multi-agent collaboration
- Human expert review
Guide development of AI agents that support complex biological investigations and target evaluation processes.
Scientific Leadership
Serve as the primary interface with disease area scientists, translational researchers, and target discovery teams.
Translate scientific challenges into AI opportunities and technical requirements.
Provide scientific oversight and ensure AI outputs remain biologically meaningful, interpretable, and actionable.
AI & Knowledge Graph Innovation
Evaluate and guide adoption of emerging approaches including:
- Knowledge graph applications
- Graph-based machine learning
- Graph-RAG architectures
- Biomedical foundation models
- Scientific reasoning systems
Identify opportunities to create reusable capabilities that can be applied across multiple therapeutic areas.
Collaboration & Delivery
Partner closely with:
- ML engineers
- Data engineering teams
- Knowledge management teams
- Research scientists
- Platform organizations
Drive prioritization and execution of AI initiatives within disease biology and target discovery programs.
Core Competencies
Deep expertise in one or more of:
- Disease biology
- Translational science
- Systems biology
- Target discovery
- Computational biology
- Biomedical informatics
Strong understanding of:
- Knowledge graphs
- Biomedical data ecosystems
- Foundation models
- Agentic AI systems
- Scientific workflow automation
Ability to connect biological questions with AI-enabled solutions.
Core Success Measures
- Scientific impact of AI-enabled target discovery workflows
- Adoption of AI capabilities by research organizations
- Quality and utility of knowledge-driven AI systems
- Reusability of solutions across disease areas
- Acceleration of biological insight generation
Preferred Qualifications
PhD in Biology, Computational Biology, Bioinformatics, Biomedical Informatics, Systems Biology, Computer Science, or related field.
Experience applying AI, machine learning, or knowledge-driven systems to biological research.
Demonstrated leadership in cross-functional scientific programs.
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