Applied AI Workflow Clinical Scientist Full-time

AI Workflow Practitioner

Drives the practical application of large language models (LLMs) and agentic artificial intelligence (AI) across the Inflammation & Immunology (I&I) portfolio by partnering closely with clinical and scientific teams to identify high-value use cases, building reusable workflows, and ensure adoption, rigor, and impact. This role sits within AI Delivery & Enablement (AIDE), the Systems Immunology line created to make AI and omics workflows practical across I&I through the conversion of repeat asks into reusable capabilities embedded in day-to-day scientific work.

The role sits at the intersection of clinical sciences and applied AI. The strongest candidate will have the clinical development experience to recognize where work can accelerate – study design, feasibility, trial conduct, clinical data review, and regulatory readiness – and clinic-omics fluency to connect those workflows to biomarker strategy, endpoint development, patient stratification, and high-dimensional data from clinical and translational studies. Near-term, the emphasis is clinical execution, where repetitive, document- and data-heavy work offers the fastest gains. Over time, the greater differentiation will come from extending those workflows into clinical omics analysis. The ideal candidate can operate across both domains, while bringing clear depth in at least one. Above all, this is a hands-on practitioner role focused on building and applying reusable AI workflows, not an AI governance or program-management role.

Role Responsibilities:

• Identify high-value, repeat use cases across I&I clinical development and translational science where LLMs, agentic AI, and workflow automation can materially improve the speed, quality, and accessibility of the work; then design, build, and refine the reusable AI tools that address them.
• Work directly with clinical scientists, clinical operations, biostatistics, translational teams, and computational biologists to understand real workflow pain points, define fit-for-purpose solutions, and iterate quickly toward tools that are scientifically useful and operationally adopted.
• Work across the Digital ecosystem to prevent duplication and deploy existing platforms where appropriate, bringing the scientific requirements and evaluation criteria that make build-or-buy decisions defensible.
• Apply the same rigor to AI that you would to any clinical or scientific method: fit-for-purpose evaluation, grounded outputs, documentation, guardrails, disclosure of model limitations, and human oversight where it matters, with particular care where workflows touch GCP-governed or otherwise regulated data.
• In the near term, concentrate on clinical execution, where study design, feasibility, data review, and submission readiness offer the fastest and most visible gains; over time, extend the same approach to clinical omics and translational data, where the capability is harder to build and holds its value longer.
• Throughout, raise AI fluency among collaborators by demonstrating practical workflows, explaining trade-offs clearly, and helping scientists build confidence in the responsible use of LLM-enabled tools.

Basic Qualifications:

• PhD with 1+ years of experience OR Master's degree and 5+ years of experience, OR Bachelor's degree and 6+ years of experience. Advanced degree in a clinical, life-science, computational-biology, or related quantitative field preferred.
• Direct experience supporting clinical development, clinical science, clinical operations, clinical data review, or regulatory science, with a strong working understanding of GCP, clinical trial conduct, and the drug development process.
• Experience with omics or other high-dimensional data from clinical or translational studies – biomarker and endpoint work, patient stratification, or exploratory and mechanistic analysis – and with translating those results into development decisions.
• Depth in both clinical execution and clinical omics is ideal; genuine depth in one with credible working knowledge of the other is acceptable.
• Recent, hands-on experience applying LLMs, agentic AI, or workflow automation in your own scientific work. For example, agent skill or instruction files you wrote, agents or assistants you stood up on an enterprise AI platform, retrieval workflows you configured, or analyses you ran with AI in the loop. Oversight of AI work done by others does not substitute; building or training models is not required.
• Demonstrated ability to build practical, reusable workflows rather than one-off analyses.
• Experience working directly with domain users to translate ambiguous needs into useful solutions, with strong collaboration and communication skills, and the ability to influence without formal authority.
• Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled clinical and scientific workflows.

Preferred Qualifications:

• Coding experience, Python or similar, sufficient to prototype, automate, and read what engineers build.
• Clinical scientist with hands-on experience in deploying AI, not just clinical exposure.
• Experience in immunology and inflammation (I&I) or an adjacent therapeutic area.
• Experience with single-cell, spatial, proteomic, or other high-dimensional platforms applied to clinical samples.
• Familiarity with scientific evidence synthesis, literature and document workflows, retrieval-augmented approaches, or multi-step workflows over structured and unstructured scientific information.
• Experience with agentic orchestration, prompt and program design, workflow automation, or multimodal AI systems.
• Familiarity with emerging regulatory expectations for AI in drug development; direct interaction with agencies such as the FDA or EMA, or contribution to submissions.
• Experience deploying AI in regulated environments, and with external technology partners, vendors, or academic collaborators.
• Strong publication or open-source record.

Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.

Additional Job Details:

• Last date to apply is September 16, 2026
• Work Location Assignment: This is a hybrid role requiring you to live within commuting distance and work on-site an average of 2.5 days per week.