Scale for data science innovation across the healthcare value chain

This is a promotional page for an on-demand Domino webinar, not a standalone case study. It poses the question of whether data science can increase the speed and likelihood of scientific discoveries reaching clinical practice, featuring a panel of Andrea de Souza (Global Head of Data Sciences & Engineering, Eli Lilly), Susan Hoang (VP Oncology Intelligence Analytics, McKesson) and Rima Alameddine (VP Enterprise Sales, Healthcare/Life Sciences, NVIDIA), moderated by Domino CEO Thomas Robinson. Discussion topics include organizational strategy and infrastructure for enterprise-scale data science, MLOps best practices across people/process/technology, and talent sourcing and development. The page contains no Eli-Lilly-specific metrics, quotes, or outcomes -- only the panel description and framing questions.

Domino Data Lab is an enterprise MLOps platform built for regulated industries — the sanctioned sandbox where biopharma teams run reproducible experiments under GxP/21 CFR Part 11 guardrails.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.