Data Scientist (AI for Computer Vision)

Your role:

1. Explore and develop innovative Artificial Intelligence (AI) algorithms for healthcare applications
2. Create and refine AI algorithms for pre- and post-processing of images and videos, focusing on data from various imaging modalities
3. Develop and implement machine learning and deep learning techniques for segmentation, classification, and statistical modeling.
4. Demonstrate expertise in image processing, object detection, segmentation, and classification.
5. Proficient in Python programming
6. Possess a strong understanding of algorithms and frameworks such as TensorFlow, PyTorch, and Keras
7. Experienced with version control systems (e.g., Git) and software development practices
8. Develop and Optimize Computer Vision Models: Design, train, and fine-tune DL models for real-world applications
9. Data Preparation & Engineering: Gather, clean, and preprocess large-scale image and video datasets for training and evaluation of computer vision models
10. Experimentation & Model Evaluation: Conduct A/B testing and assess model performance using quantitative metrics (e.g., IoU, mAP, precision, recall)
11. Research & Innovation: Stay updated with the latest advancements in computer vision, deep learning, and related technologies
12. Deployment & Scaling: Work with ML engineers to deploy models into production environments using cloud platforms (AWS, Azure) and frameworks like TensorFlow, PyTorch, and OpenCV
13. Collaboration & Communication: Work closely with cross-functional teams to integrate computer vision solutions into business processes and applications.

You're the right fit if:

1. Bachelor’s or master’s Degree: In computer science, AI, Data Science, Machine Learning, or a related field
2. Experience: 3+ years in machine learning, deep learning, or AI research, with at least 1 year of hands-on experience in developing computer vision-based AI applications
3. Programming Proficiency: Strong proficiency in Python and ML frameworks like TensorFlow and PyTorch
4. Domain Knowledge: Knowledge of computer vision, natural language processing (NLP), or multimodal AI applications
5. Technical Skills: Familiarity with computer vision techniques and fine-tuning of models
6. Problem-Solving Skills: Strong problem-solving skills and the ability to work in a fast-paced, research-driven environment
7. MLOps Tools: Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes)
8. Ethical AI: Understanding of ethical AI and bias mitigation in computer vision models.
9. Publications and Contributions: Strong publication record or contributions to open-source AI projects.