I’m searching for a data scientist who can work directly with my collection of brain-tumor image data (MRI scans) and turn it into actionable insights that assist diagnosis. The raw DICOM files are already anonymised and sorted by patient, so you can dive straight into exploration, preprocessing, and model building. Your task is to design and train a robust computer-vision pipeline—segmentation and/or classification—that highlights tumour regions and outputs clear metrics a radiologist can understand. I’m entirely open to your preferred stack; Python with PyTorch or TensorFlow is perfectly fine, and you can host experiments on Colab, a local GPU, or any cloud service you prefer. What matters most is transparent, reproducible code and well-explained results. Please include: • The full, well-commented source code and notebooks • A brief technical report explaining methodology, performance scores, and limitations • Inference instructions so I can run the model on new images without extra setup If you have prior experience with medical imaging, all the better, but I value clarity and rigor above all. Let me know the approach you intend to take, the evaluation metrics you’ll target, and the approximate timeline you’ll need once you’ve reviewed a sample of the data.