Applied AI Workflow Clinical Scientist
Pfizer
September 02, 2026
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
Role Responsibilities:
- Identify high-value, repeat use cases across I&I clinical development and translational science where LLMs, agentic AI, and workflow automation can improve speed, quality, and accessibility; design, build, and refine reusable AI tools.
- Partner with clinical scientists, clinical operations, biostatistics, translational teams, and computational biologists to understand workflow pain points, define fit-for-purpose solutions, and iterate toward tools that are scientifically useful and adopted operationally.
- Coordinate across the Digital ecosystem to avoid duplication and deploy existing platforms when appropriate; define requirements and evaluation criteria to support build-or-buy decisions.
- Apply clinical/scientific rigor to AI outputs: fit-for-purpose evaluation, grounded outputs, documentation, guardrails, disclosure of model limitations, and human oversight (especially for GCP-governed/regulated data).
- Near term: focus on clinical execution (study design, feasibility, data review, submission readiness); over time: extend to clinical omics/translational data.
- Increase AI fluency among collaborators through practical workflow demos and clear explanations of trade-offs and responsible use.
Basic Qualifications:
- PhD (1+ yrs) or Master’s (5+ yrs) or Bachelor’s (6+ yrs) in clinical/life-science/computational biology or related quantitative field.
- Direct experience in clinical development/science/operations/data review/regulatory science; strong understanding of GCP, clinical trial conduct, and drug development.
- Omics or high-dimensional clinical/translational data experience (biomarkers/endpoints, patient stratification, exploratory/mechanistic analysis) translating into development decisions.
- Recent hands-on LLMs/agentic AI/workflow automation experience built by you (no substitution via oversight; model training/building not required).
- Ability to build reusable workflows; translate ambiguous needs; strong collaboration and communication; influence without authority.
- Sound judgment on rigor, model limitations, evaluation, and human oversight.
Preferred Qualifications:
- Python (or similar) coding to prototype/automate.
- Hands-on AI deployment experience as a clinical scientist.
- I&I (immunology/inflammation) experience; single-cell/spatial/proteomic or other high-dimensional platforms.
- Evidence synthesis/literature workflows, retrieval-augmented and multi-step information workflows.
- Agentic orchestration/prompt-program design/workflow automation/multimodal AI; AI regulatory familiarity (FDA/EMA/submissions).
- Regulated environment deployment; partner/vendor/academic collaboration; strong publications or open-source record.
- Identify high-value, repeat use cases across I&I clinical development and translational science where LLMs, agentic AI, and workflow automation can improve speed, quality, and accessibility; design, build, and refine reusable AI tools.
- Partner with clinical scientists, clinical operations, biostatistics, translational teams, and computational biologists to understand workflow pain points, define fit-for-purpose solutions, and iterate toward tools that are scientifically useful and adopted operationally.
- Coordinate across the Digital ecosystem to avoid duplication and deploy existing platforms when appropriate; define requirements and evaluation criteria to support build-or-buy decisions.
- Apply clinical/scientific rigor to AI outputs: fit-for-purpose evaluation, grounded outputs, documentation, guardrails, disclosure of model limitations, and human oversight (especially for GCP-governed/regulated data).
- Near term: focus on clinical execution (study design, feasibility, data review, submission readiness); over time: extend to clinical omics/translational data.
- Increase AI fluency among collaborators through practical workflow demos and clear explanations of trade-offs and responsible use.
Basic Qualifications:
- PhD (1+ yrs) or Master’s (5+ yrs) or Bachelor’s (6+ yrs) in clinical/life-science/computational biology or related quantitative field.
- Direct experience in clinical development/science/operations/data review/regulatory science; strong understanding of GCP, clinical trial conduct, and drug development.
- Omics or high-dimensional clinical/translational data experience (biomarkers/endpoints, patient stratification, exploratory/mechanistic analysis) translating into development decisions.
- Recent hands-on LLMs/agentic AI/workflow automation experience built by you (no substitution via oversight; model training/building not required).
- Ability to build reusable workflows; translate ambiguous needs; strong collaboration and communication; influence without authority.
- Sound judgment on rigor, model limitations, evaluation, and human oversight.
Preferred Qualifications:
- Python (or similar) coding to prototype/automate.
- Hands-on AI deployment experience as a clinical scientist.
- I&I (immunology/inflammation) experience; single-cell/spatial/proteomic or other high-dimensional platforms.
- Evidence synthesis/literature workflows, retrieval-augmented and multi-step information workflows.
- Agentic orchestration/prompt-program design/workflow automation/multimodal AI; AI regulatory familiarity (FDA/EMA/submissions).
- Regulated environment deployment; partner/vendor/academic collaboration; strong publications or open-source record.