Scientific Fellow, Agentic AI (AI Co-Scientist Lead)

For this Director-level role we areseekinga scientific and technicalexpertto leadouragentic AIco-scientist initiativeacross our scientificorganization.This individual willserve as the scientific leadon the projectdefining and influencingthe strategyarchitectureevaluation framework and delivery plan for co-scientist capabilities across multiple scientific use cases and functions.This effort will spanboth internaldevelopment ofcapabilities and rigorous evaluation of externally available tools.As the senior scientificleaderof the effortthis individual willalign cross-functional stakeholdersprioritize scientificneeds and capabilitiesanddrivemeasurable progress adoption and delivery of a trusted AI capability that augments scientific reasoning hypothesis generation experimental planning and decision support.

This is a Boston based hybrid position requiring 3 days/week onsite.

Key Duties & Responsibilities:

• Serve as the senior scientific leader for theagentic AIco-scientist matrixed leadershipleada cross-functional team across scientific computational and technical areas to definepriorities translateVertex scientific needs into a sequenced roadmap and deliver scalable agentic initiativeremainsaligned topriority scientificneedsacross projects research sitesandmodalities with clear goals decision rights dependencies risks and outcomes.
• Drive scientificandtechnical leadership for internal development of co-scientist capabilities including development of scientific systemskills integrating existing scientificdatasets and methodsanddefining agentic rolesskills and orchestration patterns.
• Lead evaluation of internal and external agentic AI capabilities. Define evidence-based frameworks benchmarks and governance to assess commercial partnership open-source and internally developed options and inform build buy partner or integrate decisions.
• Establish rigorous scientific validation standardsand acceptance criteria and ongoing evaluation approaches forcorrectness relevance novelty reliability reproducibility provenance uncertainty usability andmeasurableimpact onscientificdecision-making.
• Drivedevelopment and deployment across scientific domains and modalities. Partner withscientificleadersand domain expertstoidentifyhigh-valueuse cases translatescientific workflows into agentic AI opportunities and guide delivery from prototypetosupported adoptionwhile preservinghuman scientific judgment and accountability.
• Representthe co-scientist effort with executive and senior leader stakeholders providing clear communication on strategy tradeoffs progress risks resource needs and delivery milestones withappropriate decisionescalation as needed.
• Shape agentic AI workflows and scientific operating models. Definehow agents models tools data literature compute and expert reviewwork together to support complex research questionswithappropriateplanning traceabilityescalationandreproducibility.
• Influence data platform and infrastructure withinfrastructureand platform leaders to definefoundationalrequirements for trusteddataaccess knowledge management model and tool integrationscalable compute and reliable expansionacross functions and modalities.
• Ensure delivery and adoption by driving milestone-based execution use-case prioritization change management stakeholder alignment and evidence-based decisions on where co-scientist capabilities produce meaningful scientific and organizational value.
• Drive innovationthroughtimelyknowledge ofemergingtechnologies advancements and challenges in thefield of scientificagentic AIandleveragethese external insights todrive a best-in-class AI co-scientisttool

Knowledge and Skills:

• Deepunderstanding ofdrugdiscoveryand scientific researchworkflows andhowshared AI capabilitiescan addressdomain-specific needs across functions therapeutic areas and modalities.
• Deepexpertisein modern AI including LLMs agentic systems scientific foundation models retrieval tool use planning and evaluation.
• Abilityto define rigorous evaluations benchmarks validation strategies and success measures for AI-enabledscientifictools and workflows.
• Exceptional ability to partner effectively withinfrastructure and platform collaborators throughplatform architecture tool integration scalablecompute and reproducible workflows.
• Exceptional ability to partner effectively with scientific collaborators through stronggrasp of scientificneedsin drug discovery and research scientificdata strategyandstrategic understanding of relative prioritization and impact of different opportunities.
• Strong strategic judgment executive communication stakeholder management collaboration and mentoring skills.
• Excellent presentation verbal and written communication skills including effective communication with senior leaders and cross-functional stakeholders
• Commitment to scientific rigor responsible AI reproducibility transparency data governance cybersecurity privacy andappropriate humanoversight.
• Demonstrated ability and willingness to teachengageand support others as they learnnew technologiesand concepts
• Enthusiasm for and the ability to quickly learnnew technologiesand tackle difficult problems

Educationand Experience

• Ph.D. in Computer Science Artificial Intelligence Machine Learning Computational Biology Computational Chemistry Bioinformatics Engineering Applied Mathematicswith10 years of relevantexperiencein scientific drug discovery and research or comparable training and experience.
• Seniorscientific and technical leadershipexperiencein drug discovery life sciences AI/ML computational scienceor a closely relatedfield.
• Track recordleading complex cross-functional initiatives from strategy through delivery adoption and measurable impact.
• Experiencedeveloping evaluating deploying or governing advanced AI machine learning computational or data-driven systems ina scientifically rigorous environment.
• Demonstratedability to influence senior stakeholders alignteamswithout direct authority andmakedecisions amid scientifictechnical and organizationalambiguity.
• Experience assessing external technologiesorpartnerships andrecommendingbuild buy partner adapt orintegrateoptionsbased on evidence and strategic value.
• Recognizedscientific impactthroughpublications patents deployed capabilitiesexternal presentationsor comparable contributions.

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