5 of 5 Anomaly Detection Jobs in Central London

machine learning solutions related to digital identities. Join our team and work on challenging problems in deepfake detection, bias mitigation, document understanding, anomaly detection and/or efficient ML. What y ou will be doing : Push the frontier of research in areas such as deepfake … detection, bias mitigation, fraud/anomaly detection, face matching, document understanding, and efficient on-device ML. Publish research results in national and international conferences and scientific journals. Work with product and engineering to improve our world-class identity-focused products. Representative work: Implement bias-mitigation ...

automated alerting. Enhance end-to-end pipeline resilience, monitoring, observability, failure handling, and recovery mechanisms. Integrate AI/ML features to boost reliability, anomaly detection, and operational efficiency. Collaborate closely with Data Product teams, Analytics Engineering, and Data Science to deliver production-grade data solutions. Ensure pipelines … domain platforms. Experience in enterprise data transformation programmes with stringent SLAs and reliability expectations. Preferred Experience: Exposure to AI-enabled pipeline capabilities, such as anomaly detection or intelligent monitoring. Good understanding of MLOps or supporting deployment of ML models into production environments. Experience with CI/ ...

semantic layers, Foundry-style systems) Ensure semantic compliance, data integrity and reasoning-readiness Data Quality, Observability & Lineage Implement robust data quality frameworks (validation, profiling, anomaly detection) Build observability into pipelines (lineage tracking, logging, freshness monitoring, schema drift detection) Ensure alignment with governance, security and industry ...

clients to scope, design, and deliver operationally-ready AI and ML solutions Key Responsibilities: Design, develop, and deploy machine learning models for forecasting, classification, anomaly detection, and LLM applications Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly Transition validated experiments into production-ready ...

Monitor and evaluate emerging IAM trends (e.g., passwordless authentication, decentralised identity, adaptive access controls). - Lead automation and governance initiatives, including machine-learning-based anomaly detection. - Integrate and optimise multi-factor authentication, biometrics, and mobile identity capabilities. - Drive adoption of identity threat detection and response (ITDR) solutions. ...