Senior Data Scientist

Senior Data Scientist

Senior Data Scientist – Medical Imaging

We are seeking a highly experienced and visionary Senior Data Scientist with deep expertise in deep learning , medical imaging , and hands-on experience with foundation models . This is a strategic and technical leadership role where you will work at the intersection of healthcare and artificial intelligence, building innovative AI solutions that transform medical diagnostics, workflows, and patient outcomes.

You will be instrumental in designing and developing cutting-edge AI/ML algorithms for image analysis, segmentation, classification, anomaly detection, and generative tasks in Magnetic Resonance Imaging (MRI).

Your Role:

• Lead end-to-end development of deep learning models for medical imaging tasks – from data curation and preprocessing to model training, evaluation, and deployment.

• Explore and fine-tune foundation models (e.g., vision transformers, multimodal models like CLIP, BioGPT, MedSAM) for use in diagnostic and clinical imaging applications.

• Drive research and prototyping of novel architectures for image segmentation, detection, and generation (e.g., UNet variants, GANs, autoencoders, diffusion models).

• Collaborate cross-functionally with radiologists, product managers, software engineers, and regulatory teams to ensure clinical relevance, robustness, and compliance.

• Contribute to the development of scalable ML pipelines, model interpretability tools, and performance monitoring systems.

• Publish findings in peer-reviewed journals or conferences and represent the company at scientific and industry forums.

• Mentor junior data scientists and guide the team on best practices in model development, validation, and documentation.

You are fit if:

• PhD or master's degree in computer science , Biomedical Engineering, Applied Mathematics, or a related field.
• 10+ years of experience in data science or machine learning, with at least 5 years focused on medical imaging .
• Strong experience in deep learning frameworks (TensorFlow, PyTorch) and model architectures for computer vision.
• Practical exposure to foundation models , including prompt engineering, fine-tuning, and domain adaptation.
• Proven ability to work with 2D/3D imaging datasets (DICOM, NIfTI), and medical imaging toolkits (e.g., MONAI, SimpleITK, ITK-SNAP).
• Expertise in evaluation metrics specific to medical imaging (Dice, IoU, AUC, etc.) and experience working with imbalanced datasets.
• Solid understanding of healthcare data compliance (HIPAA, FDA, MDR) and medical device AI/ML lifecycle.
• Excellent problem-solving, communication, and leadership skills.

Preferred Qualifications:

• Publications or patents in AI for healthcare or medical imaging domains.
• Experience with PACS/RIS systems, HL7/DICOM standards, and clinical workflows.
• Familiarity with LLMs or multimodal generative models in a clinical context.
• Exposure to MLOps, model deployment, and on-device inference optimization (e.g., TensorRT, ONNX, OpenVINO).

How we work together:

We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.

Onsite roles require full-time presence in the company's facilities.

Field roles are most effectively done outside of the company's main facilities, generally at the customers' or suppliers' locations.

This role is an office-based role.

About Philips:

We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

• Learn more about our business .
• Discover our rich and exciting history .
• Learn more about our purpose .

If you're interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here .