1 to 25 of 49 Permanent Anomaly Detection Jobs in London

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 & 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 surfacereal issuesand drive sustained improvement. Experience & Mindset 25+ years working with analytics, data science ...

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 ...

learning models and inference systems using complex sensor and telemetry data. Analyse multivariate time-series datasets to identify behavioural patterns and operational insights. Build anomaly detection and behavioural baselining models. Translate raw telemetry streams into meaningful, actionable intelligence. Work with streaming and low-latency data architectures … data and signal processing. Experience analysing telemetry, sensor, wearable, physiological or similar real-world datasets. Strong understanding of multivariate time-series modelling. Experience developing anomaly detection and behavioural baselining solutions. Ability to take machine learning solutions from research and experimentation into production environments. Experience solving complex problems ...

, escalation, and resolution of security alerts and major incidents. Manage, operate, and tune key security platforms, including SIEM, EDR/XDR, Network Anomaly Detection (NAD), and cloud security technologies. Oversee vulnerability management and coordinate remediation efforts across global systems. Design and operate SOAR workflows … managing major incidents and collaborating with third-party/outsourced SOC providers. Demonstrated experience designing and operating SOAR workflows. Hands-on knowledge of Network Anomaly Detection (NAD), firewalls, LAN/WAN security principles, Secure Email Gateways, and Secure Web Gateways. Solid grounding in security frameworks such ...

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 ...

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 ...

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 ...

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 ...

skills necessary for the role. 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 ...

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 ...

revenue forecasting, sales planning, or commercial operations analytics. Hands-on experience with Generative AI frameworks such as LangChain, LlamaIndex, and similar ecosystems. Experience with anomaly detection, experimentation, and causal inference techniques. Familiarity with distributed systems technologies such as RabbitMQ, Redis, Valkey, and event-driven architectures. Knowledge ...

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 ...

identity related data (e.g. -sign in- logs, access patterns, usage trends) Knowledge of AI assisted- automation use cases within IAM: Access or sign in anomaly detection Risk based- access decisions Intelligent access reviews or entitlement recommendations Experience with Microsoft security and analytics tools (e.g. Entra ID logs ...

consumers Design feature stores and data pipelines that support AI/ML model training and inference for use cases such as FX pricing, payment anomaly detection, and limit utilisation forecasting Evaluate and integrate AI‐assisted data tooling (AI‐powered cataloguing, natural language querying, automated data quality) where ...

identity related data (e.g. -sign in- logs, access patterns, usage trends) Knowledge of AI assisted-automation use cases within IAM: Access or sign in anomaly detection Risk based- access decisions Intelligent access reviews or entitlement recommendations Experience with Microsoft security and analytics tools (e.g. Entra ID logs ...

complex, regulated financial services estate. This strategy‐led and hands‐on role defines the AI/ML roadmap and delivers production‐grade models for anomaly detection, variance analysis, drift detection, market signals, forecasting, and prediction. You will work closely with engineering, data platform, and product ...

Lambda, Aurora, Airflow, Terraform). Ensure data availability, integrity, and performance as the platform scales globally. Drive best practices in data quality, automated testing, anomaly detection, and CI/CD. Partner with Product, Engineering, and Commercial teams to translate data needs into platform capabilities. EHR Integration ...

Automate connectivity services (VLAN, DNS, Firewall, DDI) integrated with ServiceNow and ITSM workflows. Drive AI/ML use cases such as WAN capacity forecasting, anomaly detection, predictive analytics, and self-healing networks. Integrate and manage observability platforms (SolarWinds Orion, Elastic, Grafana, ZDX) for proactive monitoring and insights. ...

Automate connectivity services (VLAN, DNS, Firewall, DDI) integrated with ServiceNow and ITSM workflows. Drive AI/ML use cases such as WAN capacity forecasting, anomaly detection, predictive analytics, and self-healing networks. Integrate and manage observability platforms (SolarWinds Orion, Elastic, Grafana, ZDX) for proactive monitoring and insights. ...

including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines. Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI‐assisted cloud operations. Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI‐enabled workloads. Google ...

tasks. AIOps & Intelligent Observability: Ability to integrate traditional observability stacks (e.g., Datadog, Prometheus, OpenTelemetry) with AI/ML models to automate root‐cause analysis, anomaly detection, and semantic log clustering. Self‐Healing Infrastructure Engineering: Experience designing closed‐loop, self‐healing systems that autonomously execute recovery actions (e.g. ...