Senior Multimodal AI Scientist – Computational Radiology

Responsibilities

• Lead the design, development, and validation of computational pipelines that generate robust biomarkers and predictive models from multimodal biomedical data, including imaging, clinical, molecular, and real-world datasets.
• Develop machine learning and statistical modeling approaches that identify patient subgroups, predict outcomes, and generate clinically actionable insights from high-dimensional multimodal datasets.
• Develop, implement, and support modeling solutions that interrogate complex, multimodal datasets to generate scientific and business insights, applying modern machine learning, statistical learning, representation learning, foundation models, causal inference, and related computational approaches where appropriate.
• Design and implement multimodal analytical frameworks that integrate imaging data with clinical, molecular, and other non-imaging data sources to support patient stratification and endpoint prediction.
• Research and develop predictive and explainable computational methods to guide decision-making within project parameters and established approaches.
• Present or publish findings for conferences and in peer reviewed journals.
• Builds effective relationships with established range of stakeholders to ensure utilization and value of information resources and services. Clearly and objectively communicate results, as well as their associated model assumptions, uncertainties and limitations within agreed frameworks.
• Develop, maintain, and apply ongoing knowledge and awareness in trends, standard methodology and new developments in analytics and data science.
• Implement good working practices to ensure that computational radiology work is delivered to robust quality standards and aligned to defined governance frameworks and policies.
• Collaborates in a multidisciplinary environment with world leading clinicians, data scientists and statisticians, biological experts, clinical trial delivery teams, and IT professionals.

Qualifications

• PhD in Computer Science, Statistics, Biostatistics, Machine Learning, Biomedical Engineering, Computational Biology, Bioinformatics, Applied Mathematics, Physics, or related quantitative discipline with 0-1 years of experience in the industry with a strong foundation in machine learning, statistical modeling, applied mathematics, computer vision, computational biology, bioinformatics, biomedical engineering, or related quantitative disciplines.
• Demonstrated experience building end-to-end ML pipelines including data preprocessing, model development, validation and performance assessment.
• Practical software development skills in standard data science tools: Python, R with demonstrable knowledge of good coding practices.
• Strong track record of publications in quality conferences and journals.
• Strong communication skills: ability to present compelling cases to collaborators and operate dynamically to identify solutions. Ability to work effectively within a team.