Description

Key Responsibilities :

• 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.
  
  

(ref:hirist.tech)