Applied AI Workflow Scientist, Sr. Manager
Pfizer
5 hours ago
Remote friendly (Cambridge, MA)
United States
IT
ROLE RESPONSIBILITIES:
- Identify high-value, repeat scientific and analytical use cases where LLMs, agentic AI, and workflow automation can materially improve the speed, quality, consistency, or accessibility of work across Inflammation & Immunology (I&I).
- Design, build, and refine reusable AI workflows, prompt/program structures, orchestration patterns, and agent-based tools that support end-to-end scientific narratives rather than isolated task completion.
- Partner closely with scientists, clinicians, computational biologists, and other stakeholders to understand workflow pain points, define fit-for-purpose solutions, and iterate rapidly toward tools that are scientifically useful and operationally adopted.
- Translate emerging LLM and agent capabilities into practical scientific applications, balancing speed of experimentation with methodological rigor, grounded usage, human-in-the-loop design, and reusable implementation patterns.
- Contribute to technical standards for evaluation, documentation, guardrails, and workflow quality so solutions are trusted, reproducible, and suitable for repeated use across teams and projects.
- Work across the Digital ecosystem to avoid duplication, leverage existing platforms where appropriate, and ensure solutions fit within the broader AI and data environment.
- Raise AI fluency among collaborators by demonstrating practical workflows, explaining trade-offs clearly, and helping scientists build confidence in responsible use of LLM-enabled tools.
BASIC QUALIFICATIONS:
- Bachelor’s degree and 6+ years of relevant work experience OR Master’s degree and 5+ years of experience OR PhD and 1+ years of experience. Advanced degree in computer science, ML/AI, computational biology, bioinformatics, statistics, engineering, or related field preferred.
- Strong hands-on experience applying LLMs, generative AI, machine learning, or related AI approaches to real-world workflows, products, or analytical use cases.
- Demonstrated ability to build practical, reusable workflows/systems (not one-off analyses), with strong implementation skills in Python and modern AI/ML tooling.
- Experience working directly with domain users/stakeholders to translate ambiguous needs into technical solutions; strong collaboration and communication skills.
- Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled workflows.
PREFERRED QUALIFICATIONS:
- Experience in life sciences/pharma/biotech, systems biology, immunology, translational science, omics, or related research environments.
- Familiarity with scientific evidence synthesis, literature/document workflows, retrieval-augmented approaches, or multi-step knowledge workflows using unstructured and structured scientific information.
- Experience with agentic orchestration, prompt/program design, workflow automation, or multimodal extensions of AI systems.
- Comfort operating across scientific and technical disciplines with enough domain fluency to engage credibly with scientists while maintaining an applied-AI builder mindset.
APPLICATION INSTRUCTIONS / ADDITIONAL JOB DETAILS:
- Last date to apply: April 30, 2026.
- Work location: Hybrid; live within commuting distance and work on-site an average of 2.5 days per week.
- Identify high-value, repeat scientific and analytical use cases where LLMs, agentic AI, and workflow automation can materially improve the speed, quality, consistency, or accessibility of work across Inflammation & Immunology (I&I).
- Design, build, and refine reusable AI workflows, prompt/program structures, orchestration patterns, and agent-based tools that support end-to-end scientific narratives rather than isolated task completion.
- Partner closely with scientists, clinicians, computational biologists, and other stakeholders to understand workflow pain points, define fit-for-purpose solutions, and iterate rapidly toward tools that are scientifically useful and operationally adopted.
- Translate emerging LLM and agent capabilities into practical scientific applications, balancing speed of experimentation with methodological rigor, grounded usage, human-in-the-loop design, and reusable implementation patterns.
- Contribute to technical standards for evaluation, documentation, guardrails, and workflow quality so solutions are trusted, reproducible, and suitable for repeated use across teams and projects.
- Work across the Digital ecosystem to avoid duplication, leverage existing platforms where appropriate, and ensure solutions fit within the broader AI and data environment.
- Raise AI fluency among collaborators by demonstrating practical workflows, explaining trade-offs clearly, and helping scientists build confidence in responsible use of LLM-enabled tools.
BASIC QUALIFICATIONS:
- Bachelor’s degree and 6+ years of relevant work experience OR Master’s degree and 5+ years of experience OR PhD and 1+ years of experience. Advanced degree in computer science, ML/AI, computational biology, bioinformatics, statistics, engineering, or related field preferred.
- Strong hands-on experience applying LLMs, generative AI, machine learning, or related AI approaches to real-world workflows, products, or analytical use cases.
- Demonstrated ability to build practical, reusable workflows/systems (not one-off analyses), with strong implementation skills in Python and modern AI/ML tooling.
- Experience working directly with domain users/stakeholders to translate ambiguous needs into technical solutions; strong collaboration and communication skills.
- Sound judgment regarding methodological rigor, model limitations, evaluation, and the appropriate role of human oversight in AI-enabled workflows.
PREFERRED QUALIFICATIONS:
- Experience in life sciences/pharma/biotech, systems biology, immunology, translational science, omics, or related research environments.
- Familiarity with scientific evidence synthesis, literature/document workflows, retrieval-augmented approaches, or multi-step knowledge workflows using unstructured and structured scientific information.
- Experience with agentic orchestration, prompt/program design, workflow automation, or multimodal extensions of AI systems.
- Comfort operating across scientific and technical disciplines with enough domain fluency to engage credibly with scientists while maintaining an applied-AI builder mindset.
APPLICATION INSTRUCTIONS / ADDITIONAL JOB DETAILS:
- Last date to apply: April 30, 2026.
- Work location: Hybrid; live within commuting distance and work on-site an average of 2.5 days per week.