1 to 25 of 57 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 … 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 ...

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 ...

quantitative encounters. The TaCC team has a global remit, across all products, businesses and regions in the Investment and Corporate Bank, to develop bespoke anomaly detection models. Our subject matter and datasets are complex, continually evolving. You will be responsible for driving the development of our core … Excellent programming skills, predominantly across the Python/Anaconda suite (Scikit-learn, Pandas, Numpy) Understanding financial markets, risk (for example, Know Your Client (KYC), anomaly detection/Machine Learning, project management) Excellent interpersonal skills with the ability to collaborate and partner with various teams ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

ecosystem. - Real-time trend monitoring and predictive analytics. - Customer intelligence, behavioural segmentations and personalisation. - Self-Service Business Intelligence. - Early warning systems, alerts and automated anomaly detection across payment journeys. The Data & AI Senior Product Owner is responsible for identifying gaps and opportunities for new data ...

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 ...

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. ...

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 ...

feedback, surfacing systemic issues, and influencing platform enhancements via the Services Observability Forum.* Stay current with observability trends, including AI/ML-driven insights, anomaly detection, and emerging OSS practices, and assess their applicability.* Maintain strong knowledge of observability platform features and vendor offerings to advise teams ...

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. ...

Data, Bloomberg Terminal, and/or enterprise financial data products Interest in solving problems and developing data-driven methodologies for high precision & high recall anomaly detection Past project experience using Agile/Scrum methodologies to manage complex data lifecycles If indicated, please note that years of experience ...

Code (IaC) is the organisational default and challenge inefficient legacy processes. FinOps & Governance: Enforce strict automated security and cost controls, real‐time cost‐anomaly detection, and cost estimation into the Software Development Life Cycle (SDLC) as a standard. Understanding of OpenTelemetry or other telemetry approaches. Internal Platform ...

AIOps: Translate NFRs into pragmatic architecture decisions, define SLOs/SLIs, and design modern observability (metrics, logs, traces). Apply AIOps for alert reduction, anomaly detection, incident triage, and continuous improvement across hybrid and cloud platforms. Advisory & communication: Simplify complexity for senior audiences; align diverse viewpoints ...