Associate Vice President - Applied Intelligence for Discovery (ai4d)

Eli Lilly Eli Lilly · Pharma · San Francisco, CA

Associate Vice President of Applied Intelligence for Discovery (AI4D) at Eli Lilly, responsible for the technical vision and execution of AI capabilities to transform drug discovery research. This role involves building and leading a multidisciplinary team, setting the technical roadmap for AI/ML platforms, guiding architecture decisions for end-to-end ML workflows, and driving adoption of MLOps practices. The goal is to create computationally driven discovery processes by building platforms where AI enables new workflows, moving beyond mere computational support.

What you'd actually do

  1. Build and lead a multidisciplinary team spanning data science, ML engineering, and scientific software development
  2. Set technical vision and roadmap for AI/ML platforms serving discovery research, aligning capabilities with enterprise priorities
  3. Own the full platform lifecycle — from identifying and integrating new AI capabilities to sunsetting legacy solutions, optimizing for overall portfolio performance as research priorities evolve
  4. Guide architecture decisions for end-to-end ML workflows: data pipelines, feature engineering, model development, deployment, and monitoring
  5. Serve as executive champion for AI capabilities, articulating value to senior stakeholders and influencing resource allocation

Skills

Required

  • Advanced degree (MS/PhD) in Computer Science, Data Science, Computational Biology, or related field
  • 10+ years experience in AI/ML or data science
  • 5+ years leading teams
  • Deep expertise in modern ML frameworks
  • Deep expertise in MLOps practices
  • Deep expertise in cloud platforms (AWS/Azure/GCP)
  • Ability to translate between scientific research questions and technical solutions
  • Experience navigating cross-functional dynamics and influencing without direct authority
  • Experience in pharmaceutical, life sciences, or healthcare industries
  • Track record managing external technology partnerships and vendor relationships
  • Platform architecture background

Nice to have

  • Proven track record driving AI/ML from concept to scaled deployment in complex matrixed organizations
  • Demonstrated track record of driving AI/ML capabilities from concept through scaled deployment in complex, matrixed organizations — with evidence of measurable impact on business outcomes, not just model performance

What the JD emphasized

  • proven track record driving AI/ML from concept to scaled deployment in complex matrixed organizations
  • Demonstrated track record of driving AI/ML capabilities from concept through scaled deployment in complex, matrixed organizations — with evidence of measurable impact on business outcomes, not just model performance
  • rigor required in regulated drug development

Other signals

  • building platforms where AI enables fundamentally new discovery workflows
  • drive adoption that turns platform potential into scientific impact
  • drive adoption of MLOps practices that enable reproducible, automated, and compliant AI development at scale