Applied AI and Machine Learning Scientist (director)

Pfizer Pfizer · Pharma · MA

Lead the technical evaluation, development, and application of AI across the Internal Medicine Research Unit (IMRU), translating advances in foundation models, agentic systems, multimodal AI, and related methods into reusable capabilities that strengthen scientific decision-making end-to-end. Shape the AI portfolio, define governance and evaluation standards, assess AI/ML capabilities in partnerships, and accelerate AI adoption.

What you'd actually do

  1. Provide AI/ML technical leadership for AIM2 and define a clear roadmap for how large language models, agentic systems, multimodal AI, and related methods will be applied to high-value scientific problems across Internal Medicine Research Unit (IMRU).
  2. Lead the technical evaluation and development of the AI capabilities in the AIM2, identifying, prioritizing, and shaping opportunities so that AIM2 focuses on areas where technically credible, reusable AI capabilities can create meaningful scientific or operational leverage.
  3. Provide senior technical and scientific direction across AIM2 Discovery Center, ensuring that proposed solutions are methodologically sound, fit for purpose, and grounded in biological, translational, and drug discovery context.
  4. Guide the development of reusable AI-enabled capabilities that strengthen scientific decision-making end-to-end, with emphasis on scientific rigor, technical quality, reproducibility, and practical utility across IMRU lines.
  5. Establish governance and evaluation standards for AI-built capabilities, including expectations for provenance, validation, guardrails, responsible use, and appropriate human oversight.

Skills

Required

  • AI/ML technical leadership
  • LLMs
  • agentic systems
  • multimodal AI
  • foundation models
  • generative AI
  • machine learning
  • computational biology
  • drug discovery
  • strategy development
  • portfolio prioritization
  • value realization
  • use case identification
  • solution development
  • workflow development
  • stakeholder communication
  • matrix leadership
  • governance
  • evaluation standards
  • provenance
  • validation
  • guardrails
  • responsible use
  • human oversight

Nice to have

  • life sciences
  • pharma
  • biotech
  • translational science
  • omics
  • cardiovascular biology
  • metabolic biology
  • obesity biology
  • AI adoption
  • productization
  • workflow transformation
  • regulated organizations

What the JD emphasized

  • substantial relevant experience
  • Demonstrated experience leading complex, cross-functional initiatives
  • Strong hands-on understanding of LLMs, foundation models, generative AI, machine learning
  • technical credibility
  • building and scaling reusable workflows, methods, products, or platforms rather than delivering isolated one-off analyses
  • develop strategy, shape AI portfolios
  • matrix leadership, communication, and influence skills
  • Sound judgment regarding methodological rigor, evaluation, provenance, model limitations, risk, and the appropriate role of human oversight

Other signals

  • application of AI across the Internal Medicine Research Unit
  • translating advances in foundation models, agentic systems, multimodal AI
  • reusable capabilities that strengthen scientific decision-making
  • shaping the AI portfolio
  • defining governance and evaluation standards
  • assessing AI/ML capabilities in external (or internal) partnerships
  • accelerating practical AI adoption