14 of 14 Anomaly Detection Jobs in the North of England

business domains — defining, validating, and owning the definitions, metrics and models that drive decisions.Apply machine learning to real business problems: predictive modelling, classification, segmentation, anomaly detection, and forecasting.Use LLMs and generative AI to build analytical tools, AI-assisted insight products, and intelligent automation — including prompt engineering ...

correctness are non‐negotiable Designing and enforcing data integrity frameworks across betting, trading, and customer data domains – including reconciliation between source and target systems, anomaly detection, and audit trail requirements Overseeing the implementation of regulatory and compliance reporting pipelines that meet the requirements of the UK Gambling ...

correctness are non‐negotiable Designing and enforcing data integrity frameworks across betting, trading, and customer data domains – including reconciliation between source and target systems, anomaly detection, and audit trail requirements Overseeing the implementation of regulatory and compliance reporting pipelines that meet the requirements of the UK Gambling ...

telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails and security controls (SCPs/IAM boundaries, OPA policies ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

foundation for finance.AI-augmented, intelligent finance. You own the design of the AI and machine-learning use cases that make planning better — predictive forecasting, anomaly detection, automated variance analysis, AI-generated commentary and agentic planning workflows among them — and the human-in-the-loop controls that keep ...

large remit, and we expect AI-assisted development to be embedded in how we work Apply AI tools to accelerate pipeline development, automated testing, anomaly detection and data quality monitoring Stay current with AI developments in the data engineering space and bring new approaches into the team ...

Code (IaC) is the organisational default and challenge inefficient legacy processes. FinOps & Governance: Enforce strict automated security and cost controls, real‐time cost‐anomaly detection, and cost estimation into the Software Development Life Cycle (SDLC) as a standard. Understanding of OpenTelemetry or other telemetry approaches. Internal Platform ...

Code (IaC) is the organisational default and challenge inefficient legacy processes. FinOps & Governance: Enforce strict automated security and cost controls, real‐time cost‐anomaly detection, and cost estimation into the Software Development Life Cycle (SDLC) as a standard. Understanding of OpenTelemetry or other telemetry approaches. Internal Platform ...

large remit, and we expect AI-assisted development to be embedded in how we workApply AI tools to accelerate pipeline development, automated testing, anomaly detection and data quality monitoringStay current with AI developments in the data engineering space and bring new approaches into the team's practiceIngenuity ...

contribute to a forward-thinking, collaborative environment.The Data Scientist will be responsible for building and deploying machine learning models to support personalisation, recommendations, anomaly detection, and data insights. The role involves working within modern ML Ops practices and leveraging AI-powered development tools to increase efficiency … scale.Key ResponsibilitiesDevelop and deploy machine learning models for various use cases including personalisation and anomaly detectionImplement ML Ops practices including monitoring, continuous integration, and automated retrainingUse AI-assisted development tools such as Cursor and Copilot to enhance productivityCollaborate with engineers, DevOps, and leadership to ensure robust data pipelines ...

packs; track benefits realization versus plan and highlight trade‐offs. AI‐Enabled Transformation and Engineering Partnership Identify and validate AI/ML opportunities (e.g., anomaly detection, match optimization, exception triage); design‐controlled pilots and scale proven solutions. Partner with engineering to prioritize capabilities, ensure backlog quality ...

Site Reliability Engineering (SRE) maturity, embedding monitoring, observability, automated testing and structured incident response Drive adoption of automation and AI‐enabled tooling to improve anomaly detection, incident management, vulnerability management and operational efficiency Provide technical oversight of production operations, ensuring alignment between platform standards and operational support ...