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.

Key Responsibilities

• Lead the development and deployment of advanced ML pipelines that convert raw histology images and spatial-omics datasets into quantitative, biologically interpretable insights to support target validation and drug-discovery decision making
• Design and implement end-to-end computational workflows for multi‑modal spatial data (transcriptomics, proteomics, metabolomics) across diverse tissue types, ensuring data quality, reproducibility and scalability for high‑throughput screening and validation
• Develop and apply specialised generative AI/ML methods for tissue microenvironment characterisation, cell–cell interaction modelling and disease phenotype prediction to produce quantitative evidence packages for target prioritisation and mechanism‑of‑action studies
• Collaborate with biology, analytical and clinical teams to translate computational results into actionable datasets (e.g., biomarkers, patient stratification, in vitro model readouts) that inform go/no‑go decisions
• Maintain and enhance methodological capabilities by monitoring advances in spatial multi‑omics, digital pathology and AI/ML frameworks, and by evaluating and adopting new technologies to strengthen target validation outputs
• Drive cross‑functional integration of spatial‑omics insights into broader disease phenotyping and target discovery workflows to ensure coherent data flow and interpretation across platforms
• Communicate complex analytical findings and their implications to internal and external stakeholders via clear presentations, publications and conference talks, highlighting the value of spatial biology in target validation

Qualifications & Skills

• PhD in a relevant field (e.g., Computational Biology, Systems Biology, Biology, Medicine) with a strong emphasis on machine learning and spatial multi‑omics
• Excellent programming skills with an emphasis on the Python ecosystem
• Extensive experience with spatial omics data processing workflows
• Proven track record in disease phenotyping and digital pathology, demonstrated by peer‑reviewed publications and/or AI/ML conference contributions
• Strong expertise in applied machine learning workflows, particularly with PyTorch and PyTorch‑Geometric
• Strong analytical and problem‑solving skills focused on integrating multiple data modalities to extract disease‑specific biological insights
• Familiarity with coding best practices and documentation tools (e.g., GitHub, GitLab)
• Excellent communication and collaboration skills, with the ability to work effectively in multidisciplinary teams

Find out more:

• Annual Report
• Product Pipeline

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities contact us at DE.Contact-Recruitment@gsk.com where you can also request a call.

Please note should your inquiry not relate to adjustments, we will not be able to support you through these channels. However, we have created a Recruitment FAQ guide. Click the link where you will find answers to multiple questions we receive.

GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, colour, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.

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