1 to 25 of 144 Anomaly Detection Jobs in England

About the Role Grade Level (for internal use): 11. The AIOps team is responsible for modernizing IT operations through intelligent observability, event correlation, anomaly detection, predictive insights, and automation. They partner with AIOps vendors, IT Operations, infrastructure, platform engineering, SRE, service management, and application teams to reduce … events, and topology data to improve visibility across cloud, on‐premises, and hybrid environments. Implement AIOps use cases, including noise reduction, event deduplication, correlation, anomaly detection, root cause analysis, and predictive alerting. Partner with engineering teams to build automated remediation and self‐healing workflows integrated with observability ...

engineering role, responsible for defining the AI/ML roadmap for Enterprise Solutions while also building high-rigor analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioral signals, forecasting, and prediction.The expectation is production-grade models, comparable in rigor … Experience combining statistical models, ML, semantic models, and rules-based logic where needed to achieve accuracy, stability, and explainability. Strong focus on data quality, anomaly detection, and monitoring, including metrics that surfacereal issuesand drive sustained improvement. Experience & Mindset 25+ years working with analytics, data science ...

engineering role, responsible for defining the AI/ML roadmap for Enterprise Solutions while also building high-rigor analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioral signals, forecasting, and prediction. The expectation is production-grade models, comparable in rigor … Experience combining statistical models, ML, semantic models, and rules-based logic where needed to achieve accuracy, stability, and explainability. Strong focus on data quality, anomaly detection, and monitoring, including metrics that surface real issues and drive sustained improvement. Experience & Mindset25+ years working with analytics, data science ...

user stories, acceptance criteria, and prioritized backlog items across the AIOps product lifecycle.Ownership of high-impact AIOps capabilities, including noise reduction, event management, correlation, anomaly detection, root cause analysis, predictive alerting, and self-healing remediation.Partner across Operations, infrastructure, SRE, platform engineering, service management, application teams, security … years of experience in product management, IT operations, SRE, observability, platform engineering, or related enterprise technology roles.Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation.Experience defining product roadmaps, managing requirements and backlog priorities, and delivering technical ...

stories, acceptance criteria, and prioritized backlog items across the AIOps product lifecycle. Ownership of high-impact AIOps capabilities, including noise reduction, event management, correlation, anomaly detection, root cause analysis, predictive alerting, and self-healing remediation. Partner across Operations, infrastructure, SRE, platform engineering, service management, application teams, security … experience in product management, IT operations, SRE, observability, platform engineering, or related enterprise technology roles. Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation. Experience defining product roadmaps, managing requirements and backlog priorities, and delivering technical ...

licence optimisation, hardware rightsizing, capacity rebalancing, decommissioning, and vendor renegotiation Cost Governance Gaps — Assess and strengthen cost governance processes including approval workflows, budget controls, anomaly detection, and policy enforcement Data Normalisation — Normalise cost data from disparate sources into a unified cost model, enabling consistent reporting across business … months) based on value and feasibility Reporting & Dashboards — Design and recommend live/near-real-time cost dashboards with drill-down capabilities, trend analysis, anomaly alerts, and executive reporting views FINOPS PERSONA COLLABORATION As defined by FinOps.org, this role works with all key FinOps personas: Executives ...

aerial systems Apply machine learning, data science, and RF-aware analytical techniques to electromagnetic, problem sets, including spectrum classification, emitter detection, anomaly detection and signal characterisation Explore and develop modern approaches for electromagnetic sensing and exploitation, including convolutional neural networks, transformers, autoencoders, graph-based methods ...

with engineers, analysts, and commercial stakeholders, you will translate ambiguous business problems into precise technical solutions and bring novel approaches such as graph networks, anomaly detection, and behavioural signals into production where they create real impact. Who you are End-to-end ML ownership across the full … systems in early-stage startups or small greenfield teams. This is a strong positive signal. Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning. Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices. Experience mentoring ...

fraud interventions, balancing customer experience against loss reduction. Size fraud typologies across our product lines to inform prioritization and investment decisions. Build and maintain anomaly detection systems to surface novel fraud vectors before they scale. Work closely with fraud operations, engineers, product managers, and data analysts … technical findings clearly to non-technical stakeholders and translate insights into action. What Success Looks Like in This Role: Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale. Well-designed experiments that successfully balance customer experience against ...

versus standing access, and risk-gated credential issuance (e.g. a short-lived token issued against a CrowdStrike-style risk score). Behavioural baselining/anomaly detection for workloads — defining “normal” for a recurring workflow and catching deviation at volume. SIEM integration and action attribution: distinguishing an agent … onboarding standard (e.g. mandatory MCP interfaces) and win adoption through better defaults and developer experience, not mandate alone. Design SIEM integration, behavioural baselining, and anomaly detection for agentic workflows, and centralise siloed audit logs to satisfy both security and regulatory requirements. Take bounded beachheads (for example, authenticate ...

goes next, what we retire, and what we keep. Treat the network as a system that learns. Drive the use of AI in operations: anomaly detection across flow and streaming telemetry, predictive failure analysis on optical and routing layers, automated triage and runbook execution, traffic‐engineering … billing integrations. Enough to debug jointly with the Systems team, not to own the stack. Exposure to applying ML or AI in network operations (anomaly detection on telemetry, predictive failure analysis, automated remediation, model‐informed traffic engineering). We're not looking for a researcher ...

than AI tooling alone. What you'll be doing: Machine Learning & Advanced Analytics · Develop machine learning models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection. · Apply statistical and machine learning techniques to analyse structured and semi-structured datasets. · Perform exploratory data analysis, feature engineering, model training, validation ...

data observability as a first-class platform capability, leveraging modern observability tooling (for example, Monte Carlo) for automated freshness, volume, schema, and quality monitoring, anomaly detection, and end-to-end lineage so issues are detected and resolved before they reach consumers, with clear ownership and SLAs across ...

into production environments.### Technical Skills* Python and associated data science ecosystem (Pandas, NumPy, Scikit-learn).* Machine learning techniques including classification, regression, clustering and anomaly detection.* Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.* SQL and large-scale data analysis.* Cloud-based data science platforms ...

known failure patterns, reducing human intervention and on-call burden by 50%+ Build comprehensive observability stacks (metrics, logs, traces) with ML-driven anomaly detection and AIOps capabilities Lead automated incident management: detection, triage, escalation, remediation, and post-incident review automation Drive DB automation: automated ...

through licence optimisation, hardware rightsizing, capacity rebalancing, decommissioning, and vendor renegotiationCost Governance Gaps — Assess and strengthen cost governance processes including approval workflows, budget controls, anomaly detection, and policy enforcementData Normalisation — Normalise cost data from disparate sources into a unified cost model, enabling consistent reporting across business units … months) based on value and feasibilityReporting & Dashboards — Design and recommend live/near-real-time cost dashboards with drill-down capabilities, trend analysis, anomaly alerts, and executive reporting viewsFINOPS PERSONA COLLABORATIONAs defined by FinOps.org, this role works with all key FinOps personas:Executives (CTO, CFO, CIO) — Present cost visibility ...

Product, Vendors, and other stakeholders. We’d love to see: Bloomberg Terminal, BQL, Enterprise, or Bloomberg data workflow experience. Experience productionizing AI, machine learning, anomaly detection, NLP, classification, or LLM‐assisted workflows. Experience with cloud platforms, CI/CD, automated testing, version control, metadata management, lineage ...

models from development into production environments.Technical SkillsPython and associated data science ecosystem (Pandas, NumPy, Scikit-learn).Machine learning techniques including classification, regression, clustering and anomaly detection.Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.SQL and large-scale data analysis.Cloud-based data science platforms and MLOps practices.Data ...

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 ...

ambitious collaboration between the public, charity and private sectors, designed to help people live healthier lives for longer through better prevention, earlier detection and improved treatment of diseases. We will speed up the discovery of new methods of early disease detection, and the evaluation … ideally genomic data. Developing models, algorithms or rule-based systems that support human decision-making, ideally where explainability, auditability and risk management are important. Anomaly detection, behavioural analytics, security monitoring or detection engineering. Risk quantification methods from fields such as actuarial science, epidemiology, operational research ...

regulatory considerations.Familiarity with modern data infrastructure and architecture (APIs, pipelines, cloud platforms), with exposure to AI/ML or LLM-based enrichment solutions for anomaly detection and automation.Exposure to Agile methodologies such as Scrum or Kanban.Awareness of emerging trends in Private Markets data, including the complexities ...

custom microservices Pilot and scale emerging technologies such as Generative AI where appropriate. Implementing AI/ML models for exception management, document classification, anomaly detection, and operational intelligence Identify areas for tech‐driven innovation and rapidly prototype solutions with internal or vendor teams Evaluate and select external ...

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 ...