26 to 50 of 106 Anomaly Detection Jobs in London

strategies, models and tooling, including chargeback and cost recovery, total cost of ownership and capacity planning. Experience applying statistical forecasting, time series analysis or anomaly detection to consumption or spend data. Experience articulating public cloud migration and modernisation economics to business and technology executives. Understanding of cloud ...

team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with ...

systems, third-party tools, and logs; Develop and optimize data models for reporting, monitoring, forecasting, quality assurance, and experimentation; Implement validation checks, anomaly detection, and monitoring for key datasets and metrics; Define and enforce data definitions, SLAs, and ownership for critical tables and metrics; Improve the performance ...

Build and optimize monitoring, observability, and alerting capabilities using tools such as Prometheus, Grafana, AppDynamics, and Splunk. Apply AIOps capabilities to improve event correlation, anomaly detection, root cause analysis, predictive insights, and proactive issue prevention. Partner with engineering, infrastructure, production support, security, and risk teams to ensure ...

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 ...

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 ...

incident, backup, resilience and monitoring controls. Practical experience applying AI, Agentic AI, AIOps or intelligent automation to operational use cases such as incident triage, anomaly detection, runbook automation, knowledge search, root cause support or service desk workflows. Knowledge of observability and reliability practices, including SRE principles, service ...

OpenTelemetry traces, change events, and runbooks to accelerate Mean Time to Detection (MTTD) and Resolution (MTTR). Safe Production Automation: Develop proactive anomaly detection and Human-in-the-Loop (HITL) remediation workflows with rigorous safety, security, and quality guardrails. Reliability & Scalability Engineering: Partner with application ...

Dynamics) Exposure to fraud detection, financial crime analytics, or compliance monitoring Experience applying statistical and advanced analytical techniques (e.g., hypothesis testing, regression, anomaly detection, or machine learning) Experience working with version control systems (e.g., Git) and structured development environments (e.g., VS Code or similar), with ...

scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration. We are looking for an exceptional Principal Applied … production scale Experience in computational advertising, ad tech, content moderation, trust & safety, or fraud/abuse detection Expertise in adversarial machine learning, anomaly detection, or security-oriented ML applications Familiarity with industry standards: MRC accreditation, TAG certification, brand safety frameworks, IAB content taxonomy Experience with ...

ownership and exposure to cutting-edge technologies. The Role Build scalable data pipelines using Microsoft Fabric and Databricks Develop machine learning solutions across forecasting, anomaly detection, recommendation systems, and information extraction Work with structured, semi-structured, and document-heavy datasets Create reusable data assets and analytical products ...

across data initiatives. Develop data quality, governance, metadata and data management frameworks. Explore how AI and Machine Learning can enhance data solutions through automation, anomaly detection and predictive data quality. Translate complex technical concepts and data insights into clear recommendations for senior stakeholders. Contribute to the development ...

scale data platforms. Experience in Broadband, Video, Set-Top Box, embedded devices, networking, RDK, Wi-Fi, or DOCSIS environments. Experience building telemetry-driven diagnostics, anomaly detection, root-cause analysis, observability, or intelligent troubleshooting solutions. Experience optimizing AI/LLM applications for accuracy, reliability, latency, scalability, and cost. ...

adherence to risk and control guidelines for data access and use. Data quality, reliability, and lineage - Define data contracts for telemetry sources; implement validation, anomaly detection, and reconciliation; document definitions (e.g., formulas, thresholds) and end-to-end data lineage from raw signals to KPIs and insights. Automation ...

churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning. Optimization: Building engines for marketing spend, inventory management, or resource allocation. Causal ...

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 ...

churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning. Optimization: Building engines for marketing spend, inventory management, or resource allocation. Causal ...

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 ...

applied within service management to improve operational efficiency, employee experience and service outcomes.Awareness of modern AIOps and observability capabilities, including monitoring, event correlation, anomaly detection, intelligent alerting, operational analytics and automation.Familiarity with enterprise service management platforms such as ServiceNow, Jira Service Management, Freshworks or similar technologies.Experience contributing ...

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. ...

team. Job Responsibilities: Develop and implement advanced machine learning models and algorithms to address operational challenges Deploy AI/ML solutions for process automation, anomaly detection, document intelligence, and workflow optimization Architect and oversee generative AI applications to automate and enhance business processes Build and maintain production ...

interested in how AI can improve analytical work. Hands-on experience using Gen AI for areas such as automated insight generation, AI-assisted analysis, anomaly detection or intelligent automation would be a strong advantage. Machine learning experience is also beneficial. Experience within Customer Success, Operations, Digital Experience ...

such as planning, documentation, and evidence review. Predictive Analytics & Machine Learning Experiment with ML algorithms (e.g., K-Means, Decision Trees, Isolation Forest, ARIMA) for anomaly detection, risk assessment, and forecasting. Collaborate with technical teams to integrate predictive models into audit processes. Audit Integration & Enablement Work alongside audit ...

clear judgment on when AI is appropriate versus deterministic or rules‐based approaches. Strong understanding of analytics and AI in production (e.g. data quality, anomaly detection, fraud/AML, workflow automation) and understands what it takes to run these reliably in regulated environments. Designs with security ...