Agentic AI Lead Disease Biology & Target Discovery

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.