Director/sr Director Pharmacometrics

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN +1

Director/Sr Director Pharmacometrics role at Eli Lilly focused on advancing drug discovery and development through quantitative science, with a strong emphasis on integrating AI/ML and automation into modeling workflows. The role involves scientific and technical leadership, mentoring, and shaping PMx strategy across programs and the organization, including engagement with regulatory agencies.

What you'd actually do

  1. Champion MIDD approaches across programs and platforms, defining and delivering novel PMx strategies that shape drug development decisions.
  2. Resolve complex technical and scientific challenges with cross-program impact, contributing to exposure-response, dose justification, and regulatory submission strategies in collaboration with PKPD lead and across functions.
  3. Engage proactively with regulatory agencies through meetings, responses, and advisory interactions.
  4. Inspire and elevate the scientists around you through scientific leadership, mentorship, and good examples.
  5. Champion adoption of automation tools and AI/ML approaches that deliver pragmatic, scalable value across the department and beyond.

Skills

Required

  • PhD in a relevant scientific field such as PMx, biological/pharmaceutical sciences, bioengineering, biostatistics, computer science/engineering, data science, or a related discipline.
  • 5 years post PhD experience
  • population PKPD modeling, across multiple programs or development phases
  • NONMEM and/or Monolix
  • R

Nice to have

  • mrgsolve
  • Julia
  • Phoenix NLME
  • MATLAB
  • translate quantitative modeling outputs into clear scientific and regulatory narratives
  • influencing drug development decisions and regulatory strategy through PMx analyses
  • MIDD Experience shaping technical strategy across programs or platforms
  • involvement in regulatory submissions, health authority interactions, or cross-functional governance bodies
  • mentorship and coaching skills
  • developing the capabilities of more junior scientists
  • Strong organizational and self-management skills
  • manage a complex portfolio and prioritize for maximum scientific impact

What the JD emphasized

  • AI/ML integration
  • automation
  • AI and automation in a PMx context
  • hands-on experience with tools
  • judgment to translate innovation into practical scientific value

Other signals

  • AI/ML integration
  • automation
  • model-informed drug discovery