1 to 25 of 136 Permanent Anomaly Detection Jobs in London

with many different collection techniques, along with internal QA tools. This role will specialise in taking our QA systems further: hardening the validation and anomaly detection that catches problems before our customers do, keeping our scrapers healthy and resilient as sources change under us, and shipping fast … puzzle and wants to own and raise an existing quality bar rather than maintain someone else's. Design and build data quality checks - validation, anomaly detection, freshness and coverage monitoring - across our core data pipelines Own and improve production web scraping infrastructure (Playwright, proxy management, anti ...

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 ...

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. ...

unstructured commercial and operational shipping data such as customs filings, fixtures, and SPAs. These inputs drive predictive systems that support energy-demand forecasting, anomaly detection, and real-time recommendations for physical and derivative trading. What You'll Be Doing As a Principal Data Scientist, reporting toVortexa … depth ofexpertiserather than the credential. Fluent in Python and broad in your modelling toolkit: withstrongexperience across regression and classification, clustering, time-series analysis, anomaly detection, sequence-to-sequencearchitecturesand stochasticoptimisation. Comfortable withsolvingambiguous problems:able to start with an abstract question, interrogate it, decide what is worth solving, choose ...

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 ...

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 ...

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 ...

industry experience is equally valued; expertise matters more than the credential Fluent in Python Strong experience with regression and classification, clustering, time-series analysis, anomaly detection, sequence-to-sequence architectures and stochastic optimisation Able to solve ambiguous problems independently and determine next steps without waiting … keywords Machine Learning Artificial Intelligence Python Programming Model Development Data Science Leadership ATS Optimization Keywords Hard Skills Regression Analysis Classification Clustering Time-Series Analysis Anomaly Detection Stochastic Optimisation Experiment Design Model Validation Model Deployment Monitoring Soft Skills Problem Solving Mentoring Communication Collaboration Curiosity Industry Keywords Energy Markets ...

building practical tools to identify and resolve issues before they reach customers. The Role You will: Design and develop data quality checks covering validation, anomaly detection, freshness and coverage. Own and improve production web-scraping infrastructure, including Playwright, proxies and anti-bot handling. Build and maintain data … ship solutions efficiently. About You Strong Python and SQL skills, with hands-on dbt and BigQuery experience. Proven experience designing data quality, validation and anomaly detection processes. Production experience with web scraping at scale, ideally using Playwright or an equivalent. Experience with big data sets ...

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 ...

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 ...

translating findings into practical actions. Using your expertise, you’ll design and deploy AI-driven security capabilities such as threat detection, anomaly detection and incident response. You’ll also advise on risk, compliance and regulation, ensuring we continue to meet industry standards as AI technologies ...

translating findings into practical actions. Using your expertise, you’ll design and deploy AI-driven security capabilities such as threat detection, anomaly detection and incident response. You’ll also advise on risk, compliance and regulation, ensuring we continue to meet industry standards as AI technologies ...

scalability, and ease of maintenance. AI/ML Model Development Develop, train, and evaluate machine learning models that address core FinOps use cases: cost anomaly detection, spend forecasting, workload rightsizing recommendations, and commitment coverage optimization. Implement and maintain MLOps pipelines for model versioning, automated retraining, performance monitoring … drift detection as cloud usage patterns evolve. Experiment with and apply generative AI and LLM capabilities to FinOps workflows — such as natural language interfaces for cost querying, AI‐assisted tagging remediation, or intelligent cost allocation suggestions. Collaborate with FinOps analysts to validate model outputs against business expectations, refining ...

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 ...

human identity sprawl Ensure strong monitoring, alerting, and telemetry for IAM services and integrations, and partner with SecOps where needed (SIEM, logging, anomaly detection). Drive reduction in repeat incidents through disciplined root cause analysis, prevention, and engineering partnership. Build strong partnerships across Security, Infrastructure, HR, application ...

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 ...

approach across development teams to improve monitoring consistency. Maturing from Monitoring to Full Observability Transition from basic alerting to proactive insights, leveraging AI-driven anomaly detection. Ensure comprehensive observability across frontend, backend, databases, cloud infrastructure, and networking. Implement Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Error Budgets … Observability (AIOps) % of Issues Resolved via Automated Remediation – Reduce manual intervention in incident response. Reduction in On-Call Burden (%) – Minimize alerts requiring human intervention. Anomaly Detection Accuracy (%) – Improve proactive detection of performance issues. Business & Customer Impact Customer Experience Metrics (Latency, Errors, Uptime) – Ensure observability drives ...

use.Raise reliability and data quality• Define data contracts, validation rules, freshness expectations, lineage, and service-level objectives for critical datasets.• Implement automated testing, anomaly detection, alerting, and observability across the data lifecycle.• Own production issues through diagnosis, recovery, root-cause analysis, and prevention.• Improve query performance, warehouse ...

combinations and non-human identity sprawlEnsure strong monitoring, alerting, and telemetry for IAM services and integrations, and partner with SecOps where needed (SIEM, logging, anomaly detection).Drive reduction in repeat incidents through disciplined root cause analysis, prevention, and engineering partnership.Build strong partnerships across Security, Infrastructure, HR, application ...

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 ...

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. ...

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 ...