1 to 25 of 106 Permanent Anomaly Detection Jobs in London

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 ...

across AI-driven security and the security of AI systems Architect AI-powered security solutions spanning threat detection, SIEM/SOAR modernisation, anomaly detection, automated incident response, and predictive risk modelling Define security architectures for AI pipelines, data platforms, model registries, and inference environments, embedding … security operations, cyber defence, and AI or cloud platform environments Understanding of AI/ML applications in security — threat detection, behavioural analytics, anomaly detection, and predictive risk modelling Knowledge of securing AI systems — including AI pipelines, MLOps practices, data platform security, model registries, and inference ...

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 ...

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 ...

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 ...

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 ...

Databricks, and Microsoft Fabric Deliver AI-Powered Operational Use Cases Lead the design and implementation of solutions for: Predictive maintenance Asset performance management Anomaly detection Failure prediction Root cause analysis Energy optimization Production optimization Digital worker capabilities Intelligent alarm management Autonomous operations Establish frameworks for: Data governance … Historian platforms such as OSI PI Industrial IoT architectures Edge computing solutions Knowledge of industrial AI technologies Machine learning Predictive analytics Time-series analytics Anomaly detection Computer vision Generative AI Industrial copilots Digital twins Hands-on experience with one or more of: Microsoft Azure AWS Google Cloud ...

proactive support, and intelligent self-service Monitor and troubleshoot live integrations across MIF, REST and SOAP interfaces, and scheduled processes Apply predictive monitoring and anomaly detection to identify emerging platform issues before they affect users AI-Enabled Service Delivery and Innovation Apply AI and automation to service … automation frameworks for use across client engagements Identify and develop opportunities to extend client Maximo solutions with AI-enabled capability, including predictive maintenance, anomaly detection, and intelligent decision support Document 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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...

needs to act early Apply statistical and machine‐learning techniques to spot unusual data patterns, including issues that may otherwise go unnoticed Automate detection, alerting and safe recovery of common issues, and give the wider team the tools to investigate and fix problems themselves Trace how data … with Cyber Security and Infrastructure data, such as ServiceNow, Entra ID and Active Directory Applying machine learning or AI to operational problems, such as anomaly detection AI‐assisted development with tools such as GitHub Copilot and Claude Code Streamlit and Power BI A Snowflake or architecture certification ...

reuse. Schedule, monitor, and manage data pipelines using Airflow or similar workflow orchestration tools to ensure reliable, timely data delivery. Implement validation, reconciliation, and anomaly-detection controls to maintain data completeness, accuracy, and consistency. Apply AI to automate schema inference across structured and semi-structured data sources ...

maintain automation scripts, deployment tooling, and operational workflows. Implement and support observability solutions, monitoring platforms, logging frameworks, and telemetry capabilities. Develop AIOps capabilities for anomaly detection, event correlation, alert noise reduction, and proactive issue identification. Create automated remediation processes, runbooks, and self-healing mechanisms to improve operational ...

platforms and AI workloads using metrics, logs, traces and telemetry. Establish operational insight models that support proactive decision-making and enable advanced capabilities including anomaly detection, event intelligence, noise reduction and predictive operational analytics.• AI Operations & Service Reliability: Apply reliability engineering principles to AI-enabled services, monitoring ...