Associate Vice President - Methods4Insight, Data Foundry

Position:
Associate Vice President of Methods4

Insight, Data Foundry

Reports to:

Associate Vice President of Data Foundry, Discovery Technology and Platforms, LSMD Position Summary

The Head of Methods4

Insight will lead the analytical methods and computational science pillar within Data Foundry, directing a team of domain experts spanning cheminformatics, computational structural biology, statistical modeling, and AI/ML. This leader will ensure Lilly has access to the most advanced analytical approaches across all domains critical to molecule discovery, serving both human scientists and autonomous AI agents.

This role combines deep technical breadth with strategic vision. You will maintain awareness of the state-of-the-art across multiple analytical domains—from physics-based molecular modeling to generative AI—and make informed decisions about when to adopt external methods, when to develop novel approaches, and when classical methods outperform sophisticated ones. You will partner with Architecture4

Insight to ensure analytical methods are deployed on robust, scalable infrastructure, with Scale4

Insight to integrate methods into real-time experimental workflows, and with Preparedness4

Insight to embed data quality and governance standards into analytical pipelines by design.

The ideal candidate is a recognized thought leader in computational methods for drug discovery who demonstrates excellent judgment in method selection and application, and has a proven track record delivering analytical insights that accelerate discoveries and improve decisions.

• Serve as the strategic leader for analytical methods across Data Foundry, maintaining deep awareness of cutting-edge approaches in cheminformatics, computational structural biology, statistical modeling, mathematical physics, and AI/ML, and establishing Methods4
  
  Insight as the authoritative source for guidance on analytical approaches to discovery challenges.
• Build and maintain a portfolio of analytical capabilities spanning classical methods to cutting-edge AI—including QSAR/QSPR modeling, molecular docking, free energy calculations, molecular dynamics, Bayesian experimental design, active learning, deep learning, and generative models—ensuring the right approach is matched to each scientific question.
• Make strategic build-versus-buy-versus-adopt decisions for analytical capabilities, balancing speed of adoption with need for customization, and collaborating with leading analytical teams across Lilly to ensure best practices are shared.
• Proactively identify strategic “data deserts”—areas where Lilly lacks sufficient data to answer critical questions—and develop strategies to fill these gaps through targeted in silico modeling or high-throughput experimental data generation, prioritized by scientific impact and strategic value.
• Establish rigorous frameworks for evaluating and validating analytical methods, including prospective validation protocols and impact metrics that demonstrate decisions influenced, timelines accelerated, and experimental success rates improved.
• Collaborate closely with the Frontier AI group to ensure analytical methods are designed as “agent-ready” capabilities with well-defined APIs, structured inputs/outputs, error handling, and uncertainty quantification for use by autonomous AI agents.
• Collaborate closely with Architecture4
  
  Insight, Scale4
  
  Insight, and Preparedness4
  
  Insight to ensure Methods4
  
  Insight analytical capabilities are fully integrated across all Data Foundry pillars and the broader discovery ecosystem.
• Build and lead a team of domain experts, each recognized as a thought leader in their analytical specialty, fostering a culture of intellectual curiosity, continuous learning, and innovation.
• Champion a culture of scientific rigor, reproducibility, and continuous improvement across all analytical workflows.

• Ph.D. in Cheminformatics, Computational Biology, Biophysics, Applied Mathematics, Computer Science, Statistics, or related quantitative field with strong application to drug discovery.
• 15+ years of experience developing, evaluating, and deploying analytical methods for drug discovery, with…