Philadelphia brings together data science, statistical programming, and IT leaders from top life sciences organizations to share real-world experiences and lessons learned.
Expect direct conversations on modernizing statistical computing, scaling AI in regulated environments, and turning data science investments into measurable scientific impact.
.png)
.png)
.png)
.png)
.svg)
%20(1).png)
Enterprises have the models, the talent, and the budgets. What's missing is the operational foundation to turn AI investment into measurable business impact. Hear Domino's view on what's holding regulated enterprises back and what the path forward looks like.
Few people have seen drug development from as many angles as a practicing clinician, hospital executive, and former FDA Commissioner. This session explores what AI means for the future of therapeutics, clinical trials, and the regulatory frameworks that govern them.
At BMS, data science and AI are embedded end-to-end, from early discovery to clinical development and commercialization. Learn how Domino enables teams to move from Python notebooks to production AI, scaling collaboration and impact across the enterprise.
Modernizing a statistical computing environment requires more than a technology upgrade. UCB shares its approach to evolving a legacy SCE within a regulated context, including what it took to pack up technical debt, contain scope, and put the right foundations in place across people, process, and IT.
Governance frameworks built for GenAI break down when agents start acting autonomously. Justin Johnson shares how AstraZeneca made the shift, treating governance as a leadership challenge rather than a compliance exercise, and building the agentic layer on a platform that provides the guardrails.
A top 10 pharma shares how it's moving from manual, fragmented clinical analytics to a unified statistical computing environment on Domino, reducing human error, maintaining auditability across SAS, R, and Python, and supporting regulated, day-to-day statistical programming.
Most pharma enterprises have the models. Few have the applications that move insights into action across discovery, development, and commercialization. Learn how life sciences AI leaders are closing the gap.
Modernizing a statistical computing environment requires navigating decisions that no roadmap fully prepares you for. Priya Subramaniam of Novartis will share practical lessons from the process, including what the POC surfaced, how the team is approaching validation, and what QC looks like in a regulated environment.
Vibe coding opened the door. Agentic engineering changes the game. Explore how pharma data science teams are designing human-in-the-loop checkpoints, building trust frameworks, and scaling AI development within regulated environments.
GSK's STAR program tackled open source adoption with an unconventional approach: a team of R experts who said yes to every inquiry. This session traces the journey from legacy constraints to measurable progress, including the governance, training, and support structures that made the shift stick.
Few executives have a clearer view of where pharma data science is heading than Eric Gibson of Novartis. In this closing fireside chat, he shares a candid perspective on the FDA's shift toward R-based submissions, multimodal research, and why synthetic data may be the key to unlocking AI at scale.
%20(1).webp)
