How to scale enterprise data science across drug development

Q: How did Bristol Myers Squibb scale enterprise data science across discovery, manufacturing, and commercial teams?

BMS eliminated localized analytics tools and brought their practitioners onto a unified platform tied directly to enterprise data lakes and single sign-on. Instead of forcing a rigid, top-down mandate, they focused on a "pull" strategy, using internal hackathons, highlighting career acceleration, and offering pre-configured environments to inspire organic adoption across business units.

Q: What does scaling AI actually mean for a regulated pharmaceutical company?

True scale is measured by a multiplication of trusted decisions, not by counting how many people have a license to a tool. In a pharma environment, growth only matters if teams are actively building on top of each other's validated code. If a platform's metrics are rising but every team is still writing code from scratch, you have an expensive infrastructure problem, not a scaled data science practice.

Q: How can pharma data science teams move models out of prototype and into production?

Models stall when the path through compliance and user acceptance testing is undefined. BMS solved this by mapping out an explicit, governed promotion track. Because the underlying platform handles everything from early discovery through commercial forecasting, the environment versioning, package governance, and audit trails automatically carry through the entire lifecycle.

BMS grew from fragmented local analytics to a platform supporting thousands of researchers and over 80 peer-reviewed publications. They achieved this by making reproducible, reusable workflows the default across the organization, which proves that true scale is about multiplying decisions rather than just adding users or compute.

Jacob Albrecht

Director, Intelligence Systems Lab

Bristol Myers Squibb

What you'll take away from this session

Reproducibility is a competitive advantage in pharma

Workflows that can be rerun years later with identical results protect institutional knowledge and satisfy the auditability requirements of regulated pharmaceutical environments.

Scale means reuse, not more users

BMS measures platform adoption by how many teams borrow and build on each other's patterns. Raw growth in headcount or compute spend without reuse is expensive infrastructure, not scale.

Defined promotion paths get models into production

Data science assets frequently get stuck between exploration and deployment. BMS established a clear process to move validated workflows into production.

Low-friction access to compute is a prerequisite for discovery

Locking down tool environments cuts off the initial pipeline of experimentation where early clinical and manufacturing breakthroughs happen. Broad, low-friction access to data science infrastructure is where enterprise AI capability starts.

Natural language interfaces mean any practitioner can now deploy an application

Connecting tools like large language models to platform APIs allows a researcher to move from a raw data file to an interactive analytics app via a single prompt.

Bristol Myers Squibb started building its enterprise data science capability in 2015 from a fragmented environment. Compute access was siloed, data scientists were limited by local hard drives, version control barely existed, and spinning up a basic application could take eight months. There was no shared infrastructure to move models from exploration into production.

BMS removed that friction by standardizing on Domino. Over the last decade, that single foundation has expanded to support 5,000 users across nine business units, driving 30,000 projects. Jacob Albrecht, Director of the Intelligence Systems Lab, walks through the phases of this scale:

Opening unrestricted tool access for individual sandboxing
Validating outputs with enough scientific rigor to earn corporate trust
Turning localized breakthroughs into reusable templates that any team can borrow

The practical impact of this architecture is clear. Jacob shares how robotic screening workflows combined with machine learning compressed months of manual chemistry experiments down to a single week. He also highlights a dataset built inside BMS for optical chemical structure recognition that became an external benchmark used by OpenAI to test its next-generation models.

FAQ

How did Bristol Myers Squibb scale enterprise data science across discovery, manufacturing, and commercial teams?

What does scaling AI actually mean for a regulated pharmaceutical company?

How can pharma data science teams move models out of prototype and into production?

Transform the work that matters most

See how Domino helps the world’s most regulated enterprises build, scale, and govern AI-powered applications.

Watch demo Talk to an expert