The Internal Engine

Advising on the design and development of data-driven and predictive research and development environments.

This includes strategic guidance on analytical capabilities, data integration, and infrastructure required to support future-focused scientific and organisational performance.

A female doctor or scientist interacting with a digital medical display showing human anatomy, including the brain, heart, and lungs, along with various health data and charts.

Building Predictive, Data-First R&D

Integrated R&D Platform Transformation

I support organizations re-engineering R&D verticals from legacy, siloed structures into data-first operating models. Using my proven Plan-for-Success methodology, I ensure machine learning and predictive analytics are embedded into scientific workflows. This means governed, measurable, and decision-relevant rather than added as isolated technical experiments. Also, upfront a translational audit to review data pipelines

Digital Health & Measurement Science

I advise on the design and deployment of wearables, biosensors, and home-monitoring technologies to generate regulatory-grade, real-world patient data. Drawing on my leadership of Digital Clinical Trials at GSK, I ensure that measurement science is sufficiently robust to differentiate medicines beyond controlled trial settings.

AI/ML-Ready Data Infrastructure (FAIR+T)

I design and govern FAIR+T (Findable, Accessible, Interoperable, Reusable + Traceable) data pipelines that enable advanced analytics possible at scale. This includes multimodal data integration (OMICS, imaging, clinical data), enabling capabilities such as 3D cellular visualisation and predictive discovery.

Group of scientists and doctors observing a large screen showing the number 42, with floating pills, molecular structures, and digital elements in the background.
Diagram illustrating the data pipeline process with four stages: engineering, preparation, analytics, and output, emphasizing accelerating the data pipeline.