Applied AI Workflow Clinical Scientist

Pfizer; United States; Hybrid; USD 150,000 - 240,000; Full time

Job summary

Pfizer seeks a clinician‑scientist to drive practical AI: applying LLMs and agentic AI across the Inflammation & Immunology portfolio, building reusable workflows, and ensuring adoption and impact in day‑to‑day scientific work.

The role bridges clinical development and clinical omics, with emphasis on study design, data review, and regulatory readiness, plus collaboration with biostatistics and translational teams to translate results into development decisions.

Qualifications

• Hands-on use of LLMs, agentic AI, or workflow automation in scientific work.
• Experience supporting clinical development, clinical operations, or regulatory science with GCP knowledge.
• Ability to translate ambiguous needs into practical AI solutions and collaborate across domains.

Responsibilities

• Identify high-value, repeat-use AI use cases in I&I; design, build, and refine reusable AI tools.
• Collaborate with clinical scientists, operations, biostatistics, and translational teams to define fit-for-purpose solutions.
• Work across the Digital ecosystem to prevent duplication and promote platform reuse.
• Apply rigorous evaluation, documentation, guardrails, and human oversight for AI workflows touching regulated data.
• In near term, focus on clinical execution; later extend to clinical omics and translational data.
• Raise AI fluency among collaborators by demonstrating practical workflows and trade-offs.

Skills

LLMs experience

Agentic AI

Workflow automation

Clinical development experience

Collaboration and communication

Education

PhD with 1+ years of experience OR Master's with 5+ years OR Bachelor's with 6+ years (advanced degree in clinical life‑science or computational field preferred)

Tools

Python

Job description

ROLE SUMMARY:

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 s