Agentic AI Lead – Disease Biology & Target Discovery
About this opportunity
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.
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Job details
How this role compares
Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.
We currently track 1239 comparable Drug Discovery & Preclinical Research roles across 61 biopharma companies.
Salary context
158 of 1239 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 1239 comparable roles
+ 26 more countries
Seniority mix
688 of 1239 peers have a known seniority level
Therapeutic area mix
2 of 1239 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.