Data Engineer

In the fight against breast cancer, every medical image is an opportunity: to unlock insight, to uncover risk, to embody health, to empower life. ScreenPoint’s Transpara Breast AI delivers unmatched precision for breast radiologists and helps define personalized care pathways for every woman we serve. Make your mark as part of The Breast AI Company.

To support our ambitions to expand towards multi-modality solutions and across different steps of the clinical care pathway, data is our foundation. We are therefore looking for a Data Engineer to help build, modernise, and scale ScreenPoint Medical’s data landscape.

In this role, you will play a key part in overcoming legacy data challenges while enabling new product development, innovation, and clinical research initiatives. You will work closely with Engineering, Innovation, Clinical, and Product teams to ensure that high-quality, compliant, and scalable medical imaging data is available where and when it matters.

As an AI-native company, we are looking for someone who is positive about the opportunities AI creates and comfortable using AI tools responsibly to improve the quality, speed, and effectiveness of their work.

Key responsibilities

• Design, develop, and maintain scalable data pipelines and infrastructure for medical imaging and related clinical data.
• Support the end-to-end data lifecycle, including acquisition, transfer, annotation, quality assurance, versioning, and archival of large-scale datasets from global partners.
• Improve and modernise existing (legacy) datasets to ensure consistency, traceability, and usability.
• Ensure secure, compliant, and auditable handling of data in line with regulatory requirements and ScreenPoint’s Quality Management System.
• Build and maintain tooling for data storage, visualisation, labelling, and accessibility.
• Support product development, AI training, validation, and clinical studies with reliable and well-curated datasets.
• Maintain contacts with external parties during data acquisition projects.
• Explore how AI-assisted tools can improve data engineering, documentation, analysis, and troubleshooting while maintaining strong standards for quality, security, and traceability.

Job requirements

• Master’s or PhD degree in Data Science, Computer Science, Physics, Engineering, or a related technical field.
• Experience working with complex, large-scale medical or clinical datasets.
• Strong knowledge of medical imaging data and standards (e.g. DICOM).
• Solid experience with Linux-based environments.
• Strong programming and scripting skills (e.g. Python, Bash).
• Experience with databases, data modelling, and SQL.
• Familiarity with AI/ML development workflows and clinical research environments is a strong plus.
• An AI-savvy mindset and willingness to continuously explore how AI can improve data engineering processes and ways of working.
• A hands-on, meticulous and proactive mindset with a strong sense of ownership.

Preferred qualifications

• Experience working in regulated healthcare environments.
• Familiarity with data privacy and medical device regulations (e.g. GDPR, MDR, FDA).
• Experience supporting multi-modal imaging data (e.g. mammography, tomosynthesis, MRI).

About us

ScreenPoint Medical is a leading company that develops and markets breast image analysis and cutting edge machine learning applications and services. Our product Transpara improves breast cancer survival rates by detecting cancers earlier so that treatment can be more effective and less invasive. Our product is developed with the highest quality standards and always backed with clinical evidence data.

Do you want to help us build innovative solutions to improve health worldwide? And do you want to be part of an ambitious and fast-growing team that helps you develop your career further? Please apply using the application button.

Providing a Certificate of Conduct (VOG) or background check is part of our application procedure. Questions about the contents of the vacancy or the recruitment process at ScreenPoint Medical? Please send an email to [email protected]. Please note that applications submitted via this email address will not be reviewed. To apply, please use the ‘Apply for this job’ button.