Agentic AI Lead – Disease Biology & Target Discovery

Career Category

Research

Job 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.

.