13 of 13 Anomaly Detection Jobs in the UK excluding London

building intelligent agents that detect anomalies and glitches in gameplay, ensuring an exceptional player experience. Key Responsibilities Design and implement ML models for anomaly detection in video streams and gameplay data. Apply computer vision and video analysis techniques to identify irregularities and bugs. Explore advanced methods such … background in machine learning research; PhD preferred (or equivalent experience in game development). Expertise in Python, PyTorch, and TensorFlow. Experience with video-based anomaly detection, computer vision, and streaming analysis. Gaming industry experience is a plus. Ability to lead projects and guide technical direction ...

communicate findings in clear, non-technical terms. Modelling, experimentation, and impact Build and deploy predictive and prescriptive models (predictive maintenance, yield/defect forecasting, anomaly detection, capacity planning, SPC-aware models). Deployment, governance, and scale Operationalise models in the cloud with robust MLOps practices, including versioning … monitoring, drift detection, retraining, and documentation. Create model cards and explainability artifacts to support trust and adoption by operators and leadership. Develop scalable data pipelines and reusable analytics components; enable near real-time scoring and alerting for process deviations. Collaboration, enablement, and change management Partner with process engineers ...

Deep understanding of service assurance, fulfillment, and network operations; hands-on with digital twin frameworks (topology/graph modeling, intent-based orchestration), AIOps platforms (anomaly detection, noise reduction, predictive insights), and streaming/data pipelines (Kafka, Pub/Sub, Dataflow) Familiarity with cloud-native (GCP/Azure ...

analysis. Work with the Quality team to automate FAI checks, SPC monitoring, supplier performance analysis, and NCR triage. Assist Production with digital work instructions, anomaly detection, and AI-supported training materials. Enterprise-level AI integration: Contribute to long-term AI architecture decisions choosing tools, shaping governance ...

deploy production-grade ML and GenAI solutions in high-impact operational environments. What youll be doing: Design and build ML models across forecasting, classification, anomaly detection, and GenAI/LLM use cases Lead experimentation cycles: hypothesis, evaluation, iteration, and validation Take successful ML approaches into production, including ...

scalability, and reliability. Leverage tools like Alteryx for data transformation and workflow efficiency. Data Innovation & Automation: Apply AI tools and automation to enhance reporting, anomaly detection, and performance forecasting. Support the integration of predictive analytics and natural language query tools for improved data access and insight. Enablement ...

scalability, and reliability. Leverage tools like Alteryx for data transformation and workflow efficiency. Data Innovation & Automation: Apply AI tools and automation to enhance reporting, anomaly detection, and performance forecasting. Support the integration of predictive analytics and natural language query tools for improved data access and insight. Enablement ...

Teams integrations, and telemetry based improvements. Drive AI enabled operations using Bedrock/SageMaker and the Model Context Protocol (MCP) for log analysis, anomaly detection, and incident workflows. Define cloud guardrails and enforce security controls, including IAM boundaries, SCPs, OPA policies, tagging, centralized logging, and audit tooling. ...

. Libraries such as NetworkX , TensorFlow or PyTorch , NLTK, spaCy, Hugging Face, BERT , Pandas, NumPy, scikit learn . Graph based ML familiarity: link prediction, anomaly detection, traversal, community detection. NLP/NLU skills: entity/relation recognition, summarisation, topic modelling, classification, coreference resolution. Visualization and communication Graph ...

dashboards and visualisations, communicating insights in accessible, non-technical language Modelling & Deployment Develop and deploy predictive and prescriptive models (e.g. predictive maintenance, defect forecasting, anomaly detection, capacity planning) Operationalise models using cloud and MLOps best practices, including monitoring, documentation, retraining, and explainability Collaboration & Change Work closely with ...

areas include: Data analysis of the telemetry from Ground and Space Assets to ensure reliable and efficient performance Develop operational automation for mission execution, anomaly detection and resolution, space situational awareness, and performance monitoring. Develop a data platform that allow for flexible data storage and analytics Maintain ...

Kettering depending on the project stage. Projects that are ear marked for this assignment are a Data Ops/IIoT platform, and an anomaly detection solution. There is also a work stream to install a new verification process for a Quality Compliance clause, plus integration ...