Clinical Imaging Data Scientist

Brainomix Limited
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Scientist - Data / Machine Learning

Data Scientist

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

The imaging data scientist will work within the Translational Medicine Team to deliver data analysis related to internal and external research projects. The role will report to the Senior Medical Director, but also work closely with the algorithm development team. The data scientist will be responsible for the analysis of internally held imaging and clinical datasets acquired through external collaborations. The post holder will be responsible for curating, quality checking, processing and analysing data, summarising and reporting findings, and communicating with external collaborators. The data scientist will also support internal and external research planning activities of Brainomix, as well as contribute to research publications.  

Key responsibilities

  • Curation of large imaging and clinical datasets
  • Data quality checking
  • Analysis of imaging-based and clinical research projects
  • Collaboration with external partners
  • Internal collaboration with Research and Development team to deliver research projects and feedback on algorithm design
  • Communication of scientific results with wider team and collaborators
  • Planning research and drafting research protocols
  • Reporting research results, contributing to academic and commercial outputs

Requirements

Essential Requirements:

  • Degree in relevant scientific or technical field (preferably higher degree, masters/PhD)
  • Experience working with research imaging data analysis (CT and/or MRI)
  • Experience of analysis in clinical research settings
  • Training in statistical methodology
  • Experience of working in a research team
  • Publication of peer-reviewed scientific papers

Desirable Requirements:

  • Competent in imaging handling and scripting for image processing (e.g. bash/python)
  • Familiarity with clinical research protocols
  • Excellent communication (verbal and written), including graphical content
  • Good project management skills
  • Competence with data analysis and statistical software (‘R’ or equivalent)
  • Experience with machine learning for image analysis desirable but not required

Benefits

  • Private Healthcare Plan
  • Pension Plans
  • Life Assurance
  • Employee Assistance Programme

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.