1 to 4 of 4 Anomaly Detection Jobs with Hybrid or WFH Options

various services. Data Science Use Python or R, alongside SQL to analyse and model complex, high volume data. Develop and apply clustering, classification, regression, anomaly detection, natural language processing, or GenAI models to business use cases. Apply advanced analytics to business scenarios such as pricing, CLV (including more »

as GPT-3, DALL-E, CLIP, or others. Familiarity with machine learning concepts and techniques such as supervised and unsupervised learning, classification, regression, clustering, anomaly detection, natural language processing, computer vision, and deep learning. Experience with data visualization and storytelling tools and techniques. Excellent communication, collaboration, stakeholder more »

AI and Machine Learning Models: Train and fine-tune AI and machine learning models to improve the accuracy and efficiency of the platform s anomaly detection and predictive analytics capabilities. System Integration: Collaborate with the engineering team to integrate computer vision solutions with existing systems and infrastructure … computer vision libraries and frameworks like OpenCV, TensorFlow, or PyTorch. Algorithm Development : Strong skills in developing and optimising computer vision algorithms for object detection, tracking, segmentation, and classification . Machine Learning : Solid understanding of machine learning principles and experience in training and fine-tuning models for computer vision more »

AI and Machine Learning Models: Train and fine-tune AI and machine learning models to improve the accuracy and efficiency of the platform's anomaly detection and predictive analytics capabilities. System Integration: Collaborate with the engineering team to integrate computer vision solutions with existing systems and infrastructure … computer vision libraries and frameworks like OpenCV, TensorFlow, or PyTorch. Algorithm Development : Strong skills in developing and optimising computer vision algorithms for object detection, tracking, segmentation, and classification . Machine Learning : Solid understanding of machine learning principles and experience in training and fine-tuning models for computer vision more »