1 to 25 of 48 Anomaly Detection Jobs in the UK excluding London

CrewAI). Experience with cloud platforms (AWS, Azure, GCP) or ML platforms (e.g., Databricks). Exposure to fraud detection, anti‐money laundering, anomaly detection, or graph/network‐based modelling. Understanding of model risk management, governance, and regulatory controls in financial services. ...

functionality, scalability, and efficiency of our Workday product suite. You will focus on cutting edge innovations, such as predictive analytics for workforce planning, anomaly detection in financial processes, and intelligent automation for Workday applications. You will collaborate closely with customers, mentor your team, and provide thought leadership ...

science, CRM, machine learning, and other data consumers across the organization.Own the full lifecycle of data pipelines: define SLAs, implement performance measurements, monitoring, and anomaly detection.Ensure enterprise data integrity, validation, documentation, and governance.Platform & Software EngineeringWrite production Java and Python code across our evolving platform stack — from data ingestion ...

machine learning, and other data consumers across the organization. Own the full lifecycle of data pipelines: define SLAs, implement performance measurements, monitoring, and anomaly detection. Ensure enterprise data integrity, validation, documentation, and governance. Platform & Software Engineering Write production Java and Python code across our evolving platform stack — from data ...

deploying machine learning models for risk scoring, compliance prediction, churn analysis and contractor performance analytics Building and deploying LLM-based solutions for text analytics, anomaly detection and automated compliance checks Working with Engineering and Product teams to integrate models into production environments across AWS and Azure Using ...

service provisioning.Lead SAP technical architecture decisions for hybrid and cloud-native environments (RISE, hyperscalers).Drive adoption of AIOps for SAP operations (predictive monitoring, anomaly detection, auto-remediation).Implement automation for repetitive BASIS operations (system health checks, patching, job management).Leverage AI/ML tools for incident prediction ...

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 ...

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 ...

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 ...

ecosystem.• Real-time trend monitoring and predictive analytics.• Customer intelligence, behavioural segmentations and personalisation.• Self-Service Business Intelligence.• Early warning systems, alerts and automated anomaly detection across payment journeys.The Data & AI Senior Product Specialist is responsible for identifying gaps and opportunities for new data and AI services ...

version control (Git), testing frameworks, and infrastructure as code (Terraform). Demonstrated ability to embed data quality, observability, and alerting into pipelines (automated validation, anomaly detection, monitoring). Experience leading technical design sessions, owning architecture decisions, and communicating trade offs clearly to both technical and non technical ...

driven decisions . Implement feedback loops to continuously improve model performance . AI-Driven SRE Enablement Develop ML/AI models for anomaly detection, incident prediction, and root cause analysis across distributed systems . Implement intelligent alerting systems to reduce noise and improve signal fidelity . Build ...

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 ...

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 ...

world, production problems Strong Python skills (or a similar language used in applied data science) Experience with supervised learning and unsupervised techniques such as anomaly detection Exposure to cloud‐based model training, deployment and automation A strong sense of ownership and accountability for data science outputs Experience ...

world, production problems Strong Python skills (or a similar language used in applied data science) Experience with supervised learning and unsupervised techniques such as anomaly detection Exposure to cloud‐based model training, deployment and automation A strong sense of ownership and accountability for data science outputs Experience ...

implement models that to deliver innovate actionable intelligence for Gravity users. Key Responsibilities Develop ML models for: Wi‐Fi performance prediction and classification Anomaly detection in home networks and CPE telemetry Root‐cause analysis for subscriber connectivity issues Optimization of channel selection, band steering, mesh/Extender ...

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 ...

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 ...