Head, AI Delivery & Enablement (aide), Director

Pfizer Pfizer · Pharma · MA

Head of AI Delivery & Enablement (AIDE) at Pfizer, leading strategy and technical direction for applied AI in Inflammation & Immunology. Focuses on translating advances in LLMs, agentic systems, and multimodal AI into reusable capabilities, establishing governance and evaluation standards, and driving AI adoption. Requires strong technical credibility in AI/ML and computational biology, with experience in strategy, portfolio management, and scaling reusable solutions.

What you'd actually do

  1. Set the strategic direction for AI Delivery & Enablement (AIDE) 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 Inflammation & Immunology (I&I).
  2. Lead the AIDE portfolio across I&I, identifying, prioritizing, and shaping opportunities so that AIDE focuses on areas where technically credible, reusable AI capabilities can create meaningful scientific or operational leverage.
  3. Provide senior technical and scientific direction across the AIDE line, 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 I&I lines.
  5. Establish governance and evaluation standards for AIDE-built capabilities, including expectations for provenance, validation, guardrails, responsible use, and appropriate human oversight.

Skills

Required

  • Advanced degree in computer science, machine learning, artificial intelligence, computational biology, bioinformatics, statistics, engineering, life sciences, or a related quantitative or scientific field
  • 8+ years of relevant work experience with Bachelors OR 7+ years with Master’s OR 5+ years with PhD
  • Leading complex, cross-functional initiatives in applied AI, computational science, data science, digital transformation
  • Strategy, portfolio prioritization, and value realization
  • Hands-on understanding of LLMs, generative AI, machine learning
  • Identify, prioritize, and shape high-value use cases
  • Develop strategy, shape AI portfolios, and communicate impact and return on investment to senior stakeholders
  • Matrix leadership, communication, and influence skills
  • Sound judgment regarding methodological rigor, evaluation, provenance, model limitations, risk, and the appropriate role of human oversight

Nice to have

  • Experience in life sciences, pharma, biotech, systems biology, immunology, translational science, omics, or related research environments
  • Operate fluently across AI / technology and biology

What the JD emphasized

  • strategy
  • technical direction
  • reusable capabilities
  • governance and evaluation standards
  • scientific rigor
  • technical quality
  • reproducibility
  • practical utility
  • provenance
  • validation
  • guardrails
  • responsible use
  • human oversight

Other signals

  • leading strategy and technical direction for applied AI
  • translating advances into reusable capabilities
  • shaping the AIDE portfolio
  • defining governance and evaluation standards
  • accelerating practical AI adoption