Eli Lilly

Associate Vice President leading the analytical methods and computational science pillar within Data Foundry, focusing on accelerating molecule discovery. This role involves leading a team of experts in cheminformatics, computational structural biology, statistical modeling, and AI/ML, ensuring Lilly has access to advanced analytical approaches for both human scientists and AI agents. The position requires strategic decision-making on adopting or developing methods, identifying data gaps, and establishing validation frameworks, with a strong emphasis on integrating methods into AI agent workflows.

This role focuses on applying statistical and computational approaches, including machine learning, to analyze large-scale multi-omics (genomic, proteomic, metabolomic) and clinical data from biobanks and population cohorts. The goal is to identify novel therapeutic targets and biomarkers for cardiometabolic diseases. The role involves developing and implementing bioinformatics pipelines, contributing to novel statistical methods, and collaborating with interdisciplinary teams to guide therapeutic development. While the primary focus is on data analysis and method development (L0/L2), the ultimate aim is to inform drug discovery and development.

This role focuses on applying AI/ML and automation within Chemical, Manufacturing, and Controls (CMC) disciplines in pharmaceutical R&D. The scientist will derive insights from data, build decision support tools, leverage LLMs to enhance data science workflows, and contribute to developing autonomous laboratories through AI-driven methodologies and lab automation. The primary focus is on data preparation, feature engineering, and potentially building agentic systems for experimental design and execution.

Postdoctoral Scientist role focused on developing synthesis-aware virtual screening workflows for early small-molecule drug discovery, integrating AI/ML with structure-based drug design and fragment chemistry.

Computational Biologist role focused on analyzing multimodal biological datasets (spatial and single-cell omics, proteomics, metabolomics, functional genomics) to advance target discovery in cardiometabolic diseases. The role involves developing predictive models, integrating diverse data types, applying AI/ML and causal inference methods, and influencing data architecture and analytical standards. Collaboration with internal AI and data engineering teams is key.

This role focuses on building and validating predictive models for ADMET and PK/PD endpoints within a healthcare AI/ML platform for drug discovery. It involves developing translational and distribution modeling approaches, ensuring model usability for partners, defining data strategies, and leveraging AI/agentic tools for automation and interpretability. The role also includes mentorship of junior scientists.

This role focuses on building the data infrastructure and semantic layer to make scientific data accessible for AI systems, specifically for drug discovery research. It involves designing and building data architectures, ETL/ELT pipelines, and AI-ready data products, including vector embedding pipelines for RAG. The role bridges data infrastructure and generative AI engineers, aiming to convert early deployments into repeatable system standards and evaluation practices.

The role focuses on developing and delivering enterprise-scale data products that power AI-driven insights, process optimization, and regulatory compliance within the pharmaceutical domain. It involves defining data archetypes, creating reusable data models, and implementing data frameworks for regulated environments. The core responsibility is building AI-ready data products, including training datasets for various AI/ML applications and supporting generative AI for knowledge management.