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Lead Methods4Insight to define, validate, and deploy analytical and computational methods for molecule discovery. Build/guide a multidisciplinary team, set evaluation frameworks, prioritize build-vs-buy decisions, fill strategic data gaps, and integrate agent-ready AI capabilities across Data Foundry.

Lead the Global Regulatory Affairs team, develop global regulatory strategies for oncology products, and manage submissions to ensure compliance and meet business goals.

Engineer MLOps & Scientific Platforms role at Lilly involves building ML deployment pipelines, API layers, and ensuring scientific tools are reliable and scalable.

Lead technical vision and execution for AI capabilities in drug discovery, build a multidisciplinary team, and drive organizational alignment with research priorities.

The Technical Lead will build software systems for AI-native drug discovery, design data pipelines, develop APIs, and collaborate with scientists to create efficient workflows and solutions.

The Data Architect will design and build data infrastructure to support AI-driven drug discovery, focusing on data models, architectures, and integration strategies.

The Data Architect will design data infrastructure for AI-native drug discovery, focusing on data models, ontologies, and data platform architecture to support scientific workflows and analytics.

Lead strategy, development, and execution of AI products in clinical settings, ensuring compliance and innovation while collaborating with cross-functional teams.

Lead strategy, roadmap, and execution for multiple LillyDirect digital product portfolios. Drive customer-centric design, modular architecture, technical standards, and cross-functional collaboration with engineering, design, and business partners. Own stakeholder engagement, resource prioritization, and operational excellence to deliver scalable, compliant digital health solutions and transformational outcomes.

Lead development and integration of AI/ML approaches for drug discovery, partnering with chemists and biologists to drive Go/No-Go decisions, create active learning cycles, mentor staff, and evaluate external technologies to improve ADMET and lead optimization outcomes.