Director/senior Director, Admet & Pk/pd Modeling

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN +2

This role focuses on building and validating predictive models for ADMET and PK/PD endpoints within a healthcare AI/ML platform for drug discovery. It involves developing translational and distribution modeling approaches, ensuring model usability for partners, defining data strategies, and leveraging AI/agentic tools for automation and interpretability. The role also includes mentorship of junior scientists.

What you'd actually do

  1. Build and validate predictive models across key ADMET and PK/PD endpoints — including clearance, permeability, solubility, metabolic stability, DDI risk, transporter liabilities, exposure–toxicity relationships, and PK/PD — grounded in mechanistic understanding and designed for partner use.
  2. Develop translational modeling approaches — including PBPK, IVIVE, and PK/PD simulation — to bridge preclinical and clinical settings and generate actionable predictions for biotech partners across diverse programs and modalities.
  3. Develop mechanistic and data-driven models for tissue distribution, volume of distribution, plasma protein binding, and blood-brain barrier penetration.
  4. Translate complex ADMET and PK/PD science into practical, interpretable model outputs and workflows.
  5. Leverage AI and agentic tools to automate data pipelines, improve model interpretability, and streamline delivery of outputs to partners across TuneLab's federated network.

Skills

Required

  • PhD in Pharmaceutical Sciences, Pharmacokinetics, Pharmacometrics, Clinical Pharmacology, Drug Metabolism, Toxicology, or a related STEM discipline
  • 7+ years of relevant experience in ADME/PKPD, Pharmacometrics, Translational Medicine, or a closely related field
  • Hands on experience with modelling platforms used in relevant fields such as SimCYP, GastroPlus, PK-Sim, NONMEM, Monolix, R, Matlab etc.
  • Extensive knowledge of medicinal chemistry and/or toxicology principles
  • Strong communication skills with the ability to make complex quantitative science accessible to diverse audiences.

Nice to have

  • Experience with small molecule discovery, including Beyond-Rule-of-Five and pan-modality chemotypes.
  • Track record of scientific contributions to pharmacokinetics, pharmacodynamics, ADMET, or pharmaceutical sciences.

What the JD emphasized

  • Build and validate predictive models across key ADMET and PK/PD endpoints
  • Develop translational modeling approaches
  • Develop mechanistic and data-driven models
  • Leverage AI and agentic tools

Other signals

  • AI/ML platform for drug discovery
  • democratizing access to infrastructure, expertise, and resources
  • drug discovery models trained on years of Lilly's research data
  • proprietary data obtained at a cost of over $1 billion
  • federated learning
  • machine learning algorithms
  • substantial computational power
  • exclusive datasets
  • domain-specific knowledge
  • predictive models across key ADMET and PK/PD endpoints
  • translational modeling approaches
  • mechanistic and data-driven models for tissue distribution
  • AI and agentic tools to automate data pipelines
  • improve model interpretability
  • streamline delivery of outputs to partners