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Global Pharmaceutical Organization
The Situation
A global pharmaceutical organization managing a broad portfolio of clinical trials across multiple therapeutic areas. The business had a clear strategic ambition: use historical and real-time trial data to build predictive models that could improve the success probability of new clinical programmes. The scientific case was established. What wasn't there was the engineering capability to execute on it.
The data science team was capable of building models, but the data platform and ML pipeline infrastructure required to do that at scale did not exist. Recruiting equivalent permanent talent in the life sciences sector was slow, highly competitive, and not the right solution.
What We Did
One Primero embedded specialist data engineers and MLOps practitioners directly alongside the client's existing data science team — filling the capability gap without displacing internal knowledge.
The team built a modern cloud data platform to centralize and standardize multi-source data inputs. MLOps tooling and DVC were implemented to standardize, automate, and accelerate the ML pipeline. An API framework was designed to expose ML model outputs to business users. Knowledge transfer was structured throughout.
What Followed
The client's data science team moved from blocked to operational. The ML prediction framework now operates across a broad range of clinical trial scenarios. The internal team is fully capable of operating and extending the platform independently. Time-to-capability was measured in weeks rather than the months a permanent hire would have required.
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