Associate Vice President - Methods4Insight, Data Foundry

Role Summary

Lead the analytical methods and computational science pillar within Data Foundry (Methods4Insight), directing a team of domain experts across cheminformatics, computational structural biology, statistical modeling, and AI/ML. Ensure Lilly has access to the most advanced analytical approaches across domains critical to molecule discovery, serving both human scientists and autonomous AI agents. Combine deep technical breadth with strategic vision to decide when to adopt external methods, develop novel approaches, or rely on classical methods, and partner with Architecture4Insight, Scale4Insight, and Preparedness4Insight to deploy methods across platforms with robust data governance. The ideal candidate is a recognized thought leader with a proven record delivering analytical insights that accelerate discovery.

Responsibilities

• 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 Methods4Insight 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 Architecture4Insight, Scale4Insight, and Preparedness4Insight to ensure Methods4Insight 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.

Qualifications

• Required: Ph.D. in Cheminformatics, Computational Biology, Biophysics, Applied Mathematics, Computer Science, Statistics, or related quantitative field with strong application to drug discovery.
• Required: 15+ years of experience developing, evaluating, and deploying analytical methods for drug discovery, with significant pharmaceutical or biotechnology industry experience.
• Preferred: Deep expertise in at least one analytical domain with broad understanding across all domains relevant to drug discovery.
• Preferred: Deep understanding of statistical foundations underlying both classical and modern ML methods.
• Preferred: Demonstrated thought leadership through publications, conference presentations, or other recognition as a domain expert.
• Preferred: Demonstrated success leading multidisciplinary computational teams in a matrixed, global organization.
• Preferred: Breadth across multiple computational disciplines demonstrating versatility and ability to integrate approaches.
• Preferred: Experience evaluating and adopting external methods, tools, and platforms—demonstrating ability to critically assess new approaches.
• Preferred: Strong communication and collaboration skills across scientific, technical, and executive stakeholders.
• Preferred: Track record of driving innovation in analytical methods and enabling new scientific capabilities.
• Preferred: Passion for mentoring and empowering teams in a fast-paced, mission-driven environment.

Education

• Ph.D. in Cheminformatics, Computational Biology, Biophysics, Applied Mathematics, Computer Science, Statistics, or related quantitative field with strong application to drug discovery.