ML infrastructure built for clinical trial success prediction at a global pharmaceutical organization

THE CLIENT

A global pharmaceutical organization running a large portfolio of clinical trials across multiple therapeutic areas. The organization had identified a well-defined strategic opportunity: using historical and real-time trial data to build predictive models capable of improving the success probability of new clinical programmes. 

The scientific rationale was established. The data existed. The internal data science team existed. What did not exist was the data engineering and MLOps infrastructure required to operationalise that ambition at program scale.

THE CHALLENGE

The organization's challenge was an infrastructure problem. The data science team had the domain expertise and the scientific capability to build useful models. But the prerequisite layer (a standardized, scalable data platform, a governed ML pipeline, and a framework connecting model outputs to business workflows) had not been built. 

The multi-source data landscape was complex and fragmented, beyond what the internal team could architect and integrate without specialist data engineering capability alongside them. No standardized framework existed for training, evaluating, and deploying models, meaning each modeling effort was effectively a one-off. Recruiting the permanent talent required to close this gap in the life sciences sector was slow, expensive, competitive, and disproportionate for a capability need with a defined scope.

WHAT WE BUILT

Primero Group embedded specialist data engineers and MLOps practitioners directly alongside the client's data science team, structured from the outset around a knowledge-transfer objective: the augmented team would build the infrastructure; the internal team would be fully capable of operating and extending it once the engagement concluded.

The work covered four layers: 

• A modern cloud data platform was built to centralize and standardize the multi-source data inputs the models required — resolving the fragmentation that had previously made consistent model training impractical. 
• MLOps tooling and DVC were implemented to standardize, automate, and accelerate the ML pipeline, creating a repeatable process in place of the ad-hoc approach that had existed before. 
• An API framework was designed and built to expose ML model outputs to business users — connecting prediction capability to the clinical workflows it was intended to support. 
• Throughout, structured knowledge transfer ensured the internal team understood what had been built and could operate it independently.

WHAT THIS SHOWS

This engagement represents the Group's augmentation model applied in one of the most technically and regulatorily demanding sectors in the global economy. Pharmaceutical organisations present specific challenges for external teams: complex data governance requirements, multi-source clinical data landscapes, and a regulatory environment where the integrity of ML infrastructure carries direct implications for program outcomes and compliance. The Group's ability to deploy specialist data engineering and MLOps capability into this environment — and to do so in a way that left the internal team more capable rather than more dependent — reflects the depth and the delivery discipline that the augmentation model requires at this level.