Sr. Director/Exec. Director of Cheminformatics for Early Molecule Discovery

Role Summary

Sr. Director/Executive Director of Cheminformatics for Early Molecule Discovery at Lilly. Lead from the bench to apply cheminformatics and AI/ML strategies to drive early hit-to-lead efforts, library design, and cross-functional collaboration across multiple projects in a fast-paced, collaborative environment.

Responsibilities

• Lead from the bench by applying state-of-the-art cheminformatics, ML/AI, and advanced analyses to enable library design, hit identification, prioritization, and hit-to-lead progression across multiple target classes and modalities.
• Provide scientific leadership and strategic guidance on cheminformatics and applied ML/AI approaches to drive data-driven drug discovery.
• Foster collaborations with computational colleagues, medicinal chemists, and cross-functional partners to generate screening collections, quality models, and testable hypotheses that deliver differentiated hits and leads.
• Guide the application of modern cheminformatics and ML/AI methods for library design, large dataset analysis, data mining, active learning models, hit prioritization, and ligand-/fragment-based design activities.
• Provide cheminformatics insight for new target identification and evaluation initiatives across various targets and binding modes.
• Proactively investigate new technologies to accelerate Early Molecule Discovery’s ability to prosecute challenging targets and deliver differentiated leads.
• Cultivate cross-pillar collaborations with new technology and Tech@Lilly to leverage transformative hit identification and hit-to-lead approaches.
• Develop synthon-based search strategies to leverage virtual spaces without brute-force enumeration.
• Ensure timely delivery of quality data, analyses, and robust models to project teams to accelerate hit identification and chemical series evolution.
• Communicate results and set team and organizational goals and expectations.
• Engage with external teams on early lead molecules across multiple projects and mechanisms.

Qualifications

• PhD in Cheminformatics, Computational Chemistry, or related field with 7+ years of relevant experience.
• Track record of applying and developing cheminformatics workflows that accelerate hit finding, expansion, lead generation, and library design.
• Expertise in data analytics and ML/AI modeling within cheminformatics; solid grasp of statistical principles.
• Strong scientific programming skills (Python essential) and experience with data visualizations and dashboards (e.g., Spotfire).
• Demonstrated growth mindset and ability to collaborate across computational chemistry leadership, elevating the team.
• Aptitude for building inclusive teams and mentoring early-career computational chemists.
• Ability to identify and champion new technologies for drug discovery applications.
• Proactive in establishing collaborations with medicinal chemistry and other disciplines to meet project goals and timelines.
• Effective communication with team members, cross-functional colleagues, and senior leadership.
• Proven ability to inspire and lead scientists to work across teams and sites to accelerate portfolio deliveries.

Skills

• In silico drug discovery techniques; library design; hit identification; hit expansion; lead generation.
• Cheminformatics, AI/ML methods, active learning, data mining, model development and validation.
• Collaborative skills across computational biology/chemistry and experimental teams.
• Programming in Python; data visualization and reporting.
• Communication and leadership in a matrix, cross-functional environment.

Education

• PhD in Cheminformatics, Computational Chemistry, or related field.

Additional Requirements

• Physical Demands/Travel: Travel associated with this role; work environment is laboratory-based.