Jobs at Eli Lilly and Company

Lead the development of novel AAV therapies, implement strategic vision, collaborate across departments, and manage a team for gene therapy advancements.

The Associate Director will lead project management for the ML team, driving initiatives, tracking progress, and fostering partnerships.

The position involves developing predictive models for ADMET and PK/PD, enabling biotech partners through clear model outputs, and mentoring junior scientists.

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.

The Associate Vice President oversees transactions in M&A and business development within therapeutic areas, leading teams to maximize success and drive growth initiatives.

Lead AI/ML strategy for oncology discovery, innovate data integration platforms, advance computational design, oversee experiments, and mentor a high-performing team.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

The Senior Executive Assistant provides communication, scheduling, and office management support to senior leaders while ensuring efficient operations and confidentiality.

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

Embed with discovery teams to translate scientific workflows into production AI systems. Rapidly prototype and deploy LLM/RAG-based solutions for multi-omics problems, run evaluation loops, ensure data provenance and reliability, and distill learnings into reusable platform components to drive adoption across therapeutic areas.

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

Lead development, deployment, and validation of advanced analytical methods (e.g., cheminformatics, computational biology, AI/ML) to inform discovery decisions, integrate tools with data infrastructure and autonomous agents, mentor junior scientists, publish research, and collaborate across Data Foundry pillars to accelerate molecule discovery.

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

Lead the early-stage drug discovery community, focusing on user growth, contributor partnerships, and building relationships with biotech stakeholders. Responsible for platform activation and community engagement to accelerate AI-powered drug discovery.

The Senior Principal Scientist will design and conduct experiments using various biomedical assays to support drug discovery, focusing on data analysis and high-quality reproducible results using laboratory automation.

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

Develop and apply AI/ML models (e.g., GNNs, Transformers) to drive drug discovery, collaborate with experimental scientists to iterate models and experiments, lead foundation-model development integrating molecular and biological data, publish findings, and work with MLOps/Data Engineering to transition prototypes into scalable tools.

The role requires engineering integrative solutions between AI and lab systems to enhance molecule discovery through automation and data connectivity. It involves prototyping, collaboration with scientists, deploying intelligent systems, and ensuring reliable lab operations.