Principal Scientist Spatial Multiomics

onenucleus; Cambridge; On-site; GBP 70,000 - GBP 90,000; Full time

Job summary

A leading pharmaceutical company in Cambridge is seeking a Principal Scientist to lead the design and implementation of AI/ML solutions for drug discovery. This role requires a strong background in computational biology, digital pathology, and tissue biology. The successful candidate will architect machine learning pipelines and analyze tissue microenvironments to predict disease phenotypes, contributing to innovative solutions in healthcare. Ideal for those passionate about applying technology to improve patient outcomes.

Qualifications

• Strong background in computational biology and artificial intelligence.
• Hands-on expertise in digital pathology and tissue biology.
• Experience in developing AI/ML solutions for drug discovery.

Responsibilities

• Lead design and implementation of AI/ML solutions for drug discovery.
• Architect end-to-end machine learning pipelines.
• Analyze tissue microenvironments and predict disease phenotypes.

Skills

AI/ML solutions

Digital pathology

Histology

Tissue biology

Data processing workflows

Education

PhD in Computational Biology, Bioinformatics or related field

Tools

Deep learning frameworks

Machine learning pipelines

Job description

Overview

At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people’s lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients’ needs and have the highest probability of success. We’re uniting science, technology, and talent to get ahead of disease together.

Principal Scientist (m/f/d) - Spatial Multiomics

In this position, you will join a cross‑disciplinary team to lead the design and implementation of advanced AI/ML solutions that transform raw histology images and spatial omics data into biologically interpretable, analysis‑ready datasets for drug discovery and disease phenotyping. You will architect end‑to‑end machine learning pipelines and integrate modern deep learning frameworks into data processing workflows spanning spatial transcriptomics, proteomics and metabolomics. Your work will focus on tissue microenvironment analysis, modelling cell–cell interactions and predicting disease phenotypes that directly inform target discovery and the development of complex in vitro models. The role sits at the intersection of computational biology and artificial intelligence, requiring hands‑on expertise in digital pathology, histology, and tissue biology together with practical AI/ML engineering skills to deliver scalable, robust solutions for real‑world translational impact.