Principal AI Scientist (Digital Pathology & Machine learning)

Principal AI Scientist | Digital Pathology & Machine Learning | 12 Month Contract | Fully Remote

A global Life Sciences organisation is building out its internal capability for large scale imaging data and machine learning. We are supporting them with a senior individual contributor to act as the technical authority across data ingestion, governance and model development standards. This is an advisory and hands on assessment role rather than a delivery engineering seat.

Scope

You will shape how high volume digital pathology imaging is brought into an internal big data environment at scale, covering format handling, quality checks and integrity verification. Alongside this you will define how imaging data and its associated metadata are anonymised so that filenames, embedded attributes and downstream artefacts all meet privacy and compliance expectations.

A second strand of the work is governance. The organisation needs credible standards for dataset registries, versioning, training and validation split management, model lineage and reproducibility. You will design those concepts and then apply them, reviewing AI and ML architectures, data processing workflows and proposed implementation approaches, including work produced by external vendors and project partners.

You will also advise programme stakeholders on the regulatory, compliance and quality considerations that apply to machine learning solutions in a regulated Life Sciences setting.

• MSc in computer science, statistics, computational biology or a comparable discipline. PhD welcomed.
• Minimum five years in data science, statistics or business intelligence supporting AI and ML work within clinical research, pharma, CRO or medical device environments.
• Deep Python capability across pandas, numpy, scikit learn, scikit survival, matplotlib, seaborn and shap, with disciplined use of virtual environments and version control.
• Strong grounding in machine learning methods for clustering, classification, regression, anomaly detection and optimisation, ideally across both small and large datasets.
• Working exposure to cloud platforms, with AWS or Google Cloud preferred.
• Excellent written and spoken English, and the ability to explain complex technical concepts simply to non specialist stakeholders.

12 months, starting October 2026. Fully remote. All work conducted in English. Rate dependent on experience.

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