1 to 25 of 132 Permanent Anomaly Detection Jobs in London

production deployment before a model earns trust at scale. This is a second-line position, reviewing methodologies built with scikit-learn, LightGBM, graph models, anomaly detection, and increasingly GenAI-based components. You'll also build your own tooling — agentic AI systems that read model documentation and code … regulatory expectations on model risk, fairness, and data privacy. You'll validate emerging techniques as first-line teams adopt them — graph networks, behavioral biometrics, anomaly detection, and GenAI-based systems. You'll document validation outcomes and communicate model risks directly to first-line data scientists, ML engineers ...

Security is a domain where AI genuinely matters. The attackers are using it, and the defenders need to as well. You'll work on anomaly detection, behavioural analysis, natural language processing for threat intelligence, and building interfaces that help analysts respond faster. The problems are adversarial … noisy, and the stakes are real. Responsibilities Build analyst-facing interfaces for investigating and responding to security threats Design and implement ML models for anomaly detection, behavioural analysis, and threat classification Create data pipelines that ingest and process security telemetry at massive scale Build APIs and backend ...

that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds. Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune … Domo, and Tableau specifically. Proficiency in Python or R for prototyping, and familiarity with common statistical and ML libraries and MLOps concepts. Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management. Familiarity with data product and data mesh operating models, domain-aligned ownership ...

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 ...

Design & Data Architecture Identify key signals required for: Quality & reliability Latency & throughput Cost tracking & optimisation MCP usage patterns User workflow insights Failure pattern detection Guardrail and safety event monitoring Define retention rules … considerations, anonymisation and usage policies in partnership with AI Governance and Compliance. Platform & Tooling Own requirements for dashboards, monitoring tools, model comparison views, anomaly detection alerts, and performance scorecards. dashboards monitoring tools model comparison views anomaly detection alerts performance scorecards Design systems that allow ...

high-quality inputs for models Designs, builds and trains data science products such as business models, predictive analytics tools, recommender systems, natural language processing, anomaly detection, forecasts, optimisation models and decision support systems Works with Digital teams to productionise models and deliver application front ends Translates results … high-quality inputs for models Designs, builds and trains data science products such as business models, predictive analytics tools, recommender systems, natural language processing, anomaly detection, forecasts, optimisation models and decision support systems Works with Digital teams to productionise models and deliver application front ends Translates results ...

contracts, capabilities, consumption patterns and priorities for internal and external use cases. Identify opportunities to leverage AI and machine learning to improve data classification, anomaly detection, validation, conflict resolution and other data‐intensive workflows. Work with Data Operations to move towards an exception‐based operating model, where … real‐time or near‐real‐time data processing. Experience applying AI/ML to geospatial or data‐quality problems, such as feature extraction, classification, anomaly detection or automated validation. Experience building developer‐facing products, APIs, SDKs or data services. Experience taking a platform from early‐stage architecture ...

Proof‐of‐Concept to production‐grade, fleet‐level deployments. Preferred Experience working on GPU fleet management, hyperscale infrastructure, or AI training clusters. Experience building anomaly detection or failure prediction systems for hardware or distributed systems. Experience implementing distributed straggler detection or collective‐level performance analysis … curious about distributed systems failure modes, GPU performance pathologies, and reliability engineering at scale. You’re excited by the idea of moving from anomaly detection to prediction to autonomous root cause reasoning. You enjoy designing platforms that protect uptime, revenue, and customer trust through proactive systems thinking. ...

enabled automation, proactive support, and intelligent self-serviceMonitor and troubleshoot live integrations across MIF, REST and SOAP interfaces, and scheduled processesApply predictive monitoring and anomaly detection to identify emerging platform issues before they affect usersAI-Enabled Service Delivery and InnovationApply AI and automation to service operations, accelerating … runbooks, and automation frameworks for use across client engagementsIdentify and develop opportunities to extend client Maximo solutions with AI-enabled capability, including predictive maintenance, anomaly detection, and intelligent decision supportDocument and structure support knowledge, recurring patterns, and lessons learned into reusable assets that improve service quality ...

merchant fraud, abuse, chargebacks, and anomalous behaviour. Lead the adoption of modern modelling techniques, including: Transformers and sequence models Representation learning & embeddings Autoencoders and anomaly detection Graph/network-based models Semi-supervised and weakly supervised learning Own the end-to-end model lifecycle: problem framing, feature … Technical Skills: Expert-level Python and modern ML tooling. Strong hands-on experience with: Transformers, LSTM/sequence models Representation learning & embeddings Autoencoders & anomaly detection Experience operationalising models on AWS SageMaker (or equivalent). Strong understanding of model evaluation, explainability, and monitoring in high-risk environments. What ...

time network telemetry and automation triggers. Define observability strategies covering metrics, logs, traces, and network telemetry pipelines. Architect AIOps capabilities including closed‐loop automation, anomaly detection, and predictive analytics for network operations. Integrate OSS observability with cloud‐native monitoring stacks (Prometheus, Grafana, OpenTelemetry, Elasticsearch). Lead design … automation design for network operations. Experience with observability tooling: OpenTelemetry, Prometheus, Grafana, Jaeger, Loki, ELK Stack. Knowledge of ML/AI model integration for anomaly detection, root‐cause analysis, and predictive network management. Strong grasp of TM Forum frameworks: eTOM, TAM, SID, and Open Digital Architecture ...

into end-to-end monitoring solutions. Maintain strong knowledge of observability platforms and vendor offerings and stay current with AI/ML-driven insights, anomaly detection, and emerging practices, assessing their applicability to Marex. Incident command & stakeholder management: Closely monitor relevant Jira queues and ensure tickets … environments. Excellent understanding of the process and workflow from Development, UAT, and deployment to Production. Familiarity with AI/ML‐driven triage, event correlation, anomaly detection, and alert‐tuning capabilities, and their practical application to reducing operational toil. Proven ability to troubleshoot integration issues and support observability ...

imagery, and enterprise data platforms, ensuring quality, interoperability, lineage, and spatial integrity. Design and deploy analytics and AI solutions for network analysis, geostatistical modelling, anomaly detection, predictive risk scoring, computer vision, route optimisation, and investment optimisation. Define and implement intelligent agents that support planners, engineers, field teams … analytics environments. Experience designing robust data models, pipelines, APIs, and governed data products. Hands-on understanding of machine learning and spatial analytics, including anomaly detection, predictive risk, optimisation, computer vision, and digital twin integration. Practical understanding of generative AI, retrieval-augmented generation, knowledge graphs, agents, evaluation, guardrails ...

portfolio: a unified, real-time operations platform that connects OT and IT data across assets, processes and systems, applies AI-powered insights and anomaly detection, and helps organisations turn operational signals into measurable business outcomes. The role sits at the centre of Product, Sales and Presales. Acting … recognised internal subject matter expert. Areas of focus include: Operational Intelligence and its role in improving operational performance, decision-making and reliability. Industrial AI, anomaly detection, predictive insights and operational optimisation. Asset-centric and industrial software categories, including EAM, APM, Industrial Analytics and IIoT. Ownership: Treats commercial ...

resolution Encode triage logic into automation pipelines so future instances are classified and escalated without manual intervention Apply ML and AI techniques — crash clustering, anomaly detection, pattern recognition — to build smarter triage pipelines Build debug tooling, scripts, and CI/CD integrations that surface OS-level signals … Exposure to automation frameworks, CI/CD pipelines, or scalable test systems Experience applying ML or AI techniques to systems problems — crash clustering, log anomaly detection, failure classification, or intelligent alert prioritization Familiarity with LLMs or generative AI tooling in an engineering context — prompt engineering, RAG pipelines ...

What You'll DoData Engineering & Data Products & EnablementOwn the full lifecycle of business critical data pipelines: define SLAs, implement performance measurement, monitoring, alerting, and anomaly detection, and lead incident response and root-cause analysis. Define scalable, reusable data models and curated data sets for analytics, data science ...

Enterprise Solutions, shaping high-rigor analytical capabilities across a complex, regulated global financial-services environment. Design, build, and operate production-grade models for anomaly detection, variance analysis, drift detection, forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. ...

Enterprise Solutions, shaping high-rigor analytical capabilities across a complex, regulated global financial-services environment. Design, build, and operate production-grade models for anomaly detection, variance analysis, drift detection, forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. ...

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 Executives (CTO, CFO, CIO) — Present cost visibility dashboards, business cases for optimisation, and FinOps maturity roadmaps ...

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 ...

identify opportunities for analysis, modelling or product use. Develop, compare and validate appropriate statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, similarity or extraction problems. Use NLP, embeddings and LLM-assisted techniques responsibly for classification, entity resolution, information extraction, clustering and relationship discovery ...

communicating risk to executives while serving as a senior security advocate for sales, customer audits, and incident communications. You will own full-lifecycle detection and response (telemetry, automation, CI/CD detection-as-code, and MTTR/ATT&CK metrics) and serve as Incident Commander, running … team exercises and postmortem improvements. A major focus is driving AI security and internal AI governance: integrating LLMs/ML into SOC triage and anomaly detection, defending AI attack surfaces (agent activity, prompt injection, machine identities), hardening against AI-driven threats (phishing-resistant MFA, supply chain/ ...

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 ...

from development into production environments. 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 ...