1 to 25 of 114 Permanent Anomaly Detection Jobs in London

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 surface real issues and drive sustained improvement. Experience & Mindset25+ 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 … 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 & Mindset\*\* + 25+ years working with analytics, data ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

business priorities.Build and optimize monitoring, observability, and alerting capabilities using tools such as Prometheus, Grafana, AppDynamics, and Splunk.Apply AIOps capabilities to improve event correlation, anomaly detection, root cause analysis, predictive insights, and proactive issue prevention.Partner with engineering, infrastructure, production support, security, and risk teams to ensure developed ...

analytics, model risk governance, and regulatory expectations for pricing/risk models. Hands‐on ML/AI for quant finance (signal extraction, surrogate modelling, anomaly detection), including MLOps, drift monitoring, and explainability. Knowledge of portfolio optimisation, hedging algorithms, execution analytics, and transaction cost modelling. Familiarity with distributed ...

preventative controls. Drive adoption of automation-first principles, reducing dependency on human-driven processes. Contribute to AI-driven operational use cases, including event correlation, anomaly detection, noise reduction, operational insights, and automated remediation. Ensure AIOps capabilities are grounded in reliable telemetry, clear control boundaries, and measurable operational … Operational Excellence Define standards for telemetry, monitoring, alerting, and operational visibility across all critical systems. Ensure services are observable, measurable, and support proactive detection of issues. Improve operational readiness, incident response effectiveness, and time-to-recovery through engineering solutions. CI/CD & Platform Integration Contribute to the design ...

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 ...

/v4 Foundation or experience aligning QA with service management practices. Experience applying AI/ML in Quality Engineering, including test optimisation, defect prediction, anomaly detection or analytics-led assurance. Experience with enterprise orchestration tools such as Control-M, Autosys or equivalent scheduling platforms. Experience with master ...

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 ...