Lead the design, development, and optimization of deep learning models for medical image analysis, including tasks like segmentation, classification, object detection.
Provide technical leadership and mentorship to junior data scientists and engineers, fostering best practices in research methodology, coding standards, and documentation.
Perform advanced error analysis, bias assessment, and domain generalization studies to ensure robustness across diverse populations and imaging devices.
Guide data storage, preprocessing and annotation strategies, collaborating with clinical experts to ensure data quality and clinical validity.
Collaborate cross-functionally with radiologists, regulatory teams, and product stakeholders to align AI development with clinical requirements and compliance standards.
Experience
3+ years Data Science/Machine Learning experience.
Required Skills
Strong programming skills in Python and familiarity with data science and image processing libraries (e.g., NumPy, pandas, scikit learn, opencv, PIL).
Strong fundamental knowledge of machine learning, computer vision and image processing.
Hands-on experience with deep learning frameworks like Keras or PyTorch.
Demonstrated experience with both CNNs and transformer-based architectures (e.g., ViT, Swin) for segmentation, detection, and classification tasks.
Strong background with model evaluation and error analysis.
Ability to bridge data science with engineering tasks, ensuring smooth integration of AI models into production workflows.
Hands-on experience with model optimization techniques (e.g distillation, ONNX conversion) for efficient inference.
Hands-on experience with containerization tools like Docker
Desired Skills
Familiarity with healthcare/radiology datasets, DICOM standards, and PACS integration.
Experience with domain adaptation, and robustness strategies for cross-site/cross-device generalization.
Experience with scalable backend development for AI applications.
Qualification
Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field.
