A global biopharmaceutical company developing medicines across immunology, oncology and neuroscience is looking for an experienced scientist to join its Immunology Discovery Pathology team.

The team combines tissue biology, computational methods and advanced imaging to investigate disease mechanisms, support target validation and inform drug discovery.

About the role
The company is seeking a computational image analysis specialist with expertise in digital pathology, computer vision and machine learning.

The scientist will independently develop methods for analysing tissue images and complex spatial datasets, translating them into quantitative insights about cell populations, tissue structure and disease biology.

Working alongside pathologists, immunologists, computational biologists and discovery scientists, the successful candidate will connect analytical innovation with practical research questions.

Key responsibilities
Design and validate machine-learning and computer-vision methods for tissue and cell segmentation, classification, feature extraction and representation learning.
Build reliable workflows for multiplexed imaging data, including cell identification, phenotyping and spatial analysis.
Evaluate model accuracy and generalisability using independent test datasets and appropriate metrics, including intersection over union (IoU) and F1 scores.
Develop approaches that combine information from H&E images, multiplex immunofluorescence, spatial proteomics and spatial transcriptomics.
Create Python tools to automate processing and improve compatibility between imaging systems, analytical software and data formats.
Collaborate with scientific colleagues to check data quality, interpret findings and confirm their biological relevance.
Troubleshoot complex analytical problems and deliver reproducible results within project timelines.
Advise multidisciplinary teams on computational pathology methods and their research applications.
Assess emerging AI and image-analysis techniques for use in drug discovery.
Present findings clearly and maintain thorough documentation of code, methods, parameters and results.
Required qualifications and experience
A PhD in computational biology, biomedical engineering, computer science, AI, machine learning, computer vision, biomedical image analysis or a related quantitative field.
At least four years of relevant applied experience.
Strong knowledge of mathematics, statistics, image processing, computer vision and machine learning.
Demonstrated experience developing and validating methods for analysing H&E, immunohistochemistry or multiplexed tissue images.
Strong Python programming skills, with experience using relevant tools such as NumPy, pandas, SciPy, scikit-learn, OpenCV, PyTorch or TensorFlow.
Practical experience with high-dimensional imaging datasets, such as multiplex immunofluorescence or imaging mass cytometry.
Experience creating reproducible analytical workflows using version-controlled code, organised datasets and clear documentation.
Familiarity with digital pathology platforms such as QuPath or Visiopharm, together with the ability to develop custom methods when required.
Strong communication and problem-solving skills, with the ability to work independently and collaborate across scientific disciplines.
Additional experience welcomed
Knowledge of immunology and tissue biology, particularly rheumatoid arthritis, inflammatory bowel disease or related inflammatory conditions.
Understanding of spatial omics and tissue-based techniques, including immunohistochemistry, immunofluorescence and in situ hybridisation.
Experience integrating histopathology with spatial protein and gene-expression data.
Expertise in deep learning or representation learning applied to computational pathology.
Familiarity with AWS, cloud computing or high-performance computing environments.
Experience with R and analytical or visualisation tools such as GraphPad Prism, Spotfire or Excel.