1 to 5 of 5 Permanent Anomaly Detection Jobs with Hybrid or WFH Options

experience in secure network perimeter design including network segmentation Packet capture and analysis experience using vendor and open-source tooling SIEM log analysis and anomaly detection experience (preferable) Coding and automation experience Python and/or Terraform (preferable) CISSP certifications or familiarity with CISSP concepts Hybrid working more »

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 »

are based on individual merchant risk and are charged as a percentage of payment volumes. Underpinning these products are four core types of models: Anomaly detection - highlighting where payments data suggests a statistically significant change in behaviour has occurred, AI (large language) models which ingest data from … research and development opportunities. For example payments data is a largely untapped, but hugely rich source of information for predicting the merchants credit risk. Anomaly detection in an industry where seasonality and customer behavioural changes cause lots of ‘noise’ is uniquely challenging. And predicting the level of 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 »