Career CategoryResearchJob DescriptionPosition OverviewThe 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 ResponsibilitiesScientific AI StrategyDevelop and execute a roadmap for AI-enabled capabilities supporting:Disease biology researchTarget identification and prioritizationMechanistic biologyBiomarker discoveryLiterature synthesisEvidence generationTranslational science workflowsIdentify opportunities where AI can improve scientific reasoning, evidence integration, and discovery productivity.Knowledge-Driven AI SystemsLead development of AI solutions that leverage:Knowledge graphsBiomedical ontologiesScientific literatureInternal research dataExternal biological databasesDefine approaches for integrating structured and unstructured knowledge into AI-assisted scientific workflows.Agentic Workflow DesignDesign intelligent workflows that combine:Knowledge retrievalScientific reasoningEvidence synthesisHypothesis generationMulti-agent collaborationHuman expert reviewGuide development of AI agents that support complex biological investigations and target evaluation processes.Scientific LeadershipServe 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 InnovationEvaluate and guide adoption of emerging approaches including:Knowledge graph applicationsGraph-based machine learningGraph-RAG architecturesBiomedical foundation modelsScientific reasoning systemsIdentify opportunities to create reusable capabilities that can be applied across multiple therapeutic areas.Collaboration & DeliveryPartner closely with:ML engineersData engineering teamsKnowledge management teamsResearch scientistsPlatform organizationsDrive prioritization and execution of AI initiatives within disease biology and target discovery programs.Core CompetenciesDeep expertise in one or more of:Disease biologyTranslational scienceSystems biologyTarget discoveryComputational biologyBiomedical informaticsStrong understanding of:Knowledge graphsBiomedical data ecosystemsFoundation modelsAgentic AI systemsScientific workflow automationAbility to connect biological questions with AI-enabled solutions.Core Success MeasuresScientific impact of AI-enabled target discovery workflowsAdoption of AI capabilities by research organizationsQuality and utility of knowledge-driven AI systemsReusability of solutions across disease areasAcceleration of biological insight generationPreferred QualificationsPhD 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..