Data Scientist

As a Deep Learning Engineer for medical image analysis, your role will involve developing and optimizing deep learning models for tasks such as segmentation, classification, and object detection. You will also be responsible for preprocessing and cleaning medical imaging datasets to improve AI model performance. Additionally, conducting model evaluation, error analysis, and performance benchmarking will be crucial to enhance accuracy and generalization. Collaboration with radiologists and domain experts is essential to refine AI model outputs for clinical relevance. Key Responsibilities: - Develop and optimize deep learning models for medical image analysis, including segmentation, classification, and object detection. - Preprocess and clean medical imaging datasets to enhance AI model performance and reliability. - Conduct model evaluation, error analysis, and performance benchmarking to improve accuracy and generalization. - Collaborate with radiologists and domain experts to refine AI model outputs and ensure clinical relevance. Required Skills: - Strong fundamental knowledge of machine learning, computer vision, and image processing. - Demonstrable experience training convolutional neural networks for segmentation and object detection. - Strong programming skills in Python and familiarity with data science and image processing libraries (e.g., NumPy, pandas, scikit-learn, opencv, PIL). - Hands-on experience with deep learning frameworks like Keras or PyTorch. - Experience with model evaluation and error analysis. Desired Skills: - Familiarity with healthcare or radiology datasets. - Familiarity with ONNX format. Qualification: - Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field. Please note that the job description is sourced from hirist.tech.,