1 to 25 of 29 Permanent Anomaly Detection Jobs in London

About the Role Grade Level (for internal use): 11. The AIOps team is responsible for modernizing IT operations through intelligent observability, event correlation, anomaly detection, predictive insights, and automation. They partner with AIOps vendors, IT Operations, infrastructure, platform engineering, SRE, service management, and application teams to reduce … events, and topology data to improve visibility across cloud, on‐premises, and hybrid environments. Implement AIOps use cases, including noise reduction, event deduplication, correlation, anomaly detection, root cause analysis, and predictive alerting. Partner with engineering teams to build automated remediation and self‐healing workflows integrated with observability ...

Role We are seeking a Data Scientist to work on advanced analytical solutions across biometric and geospatial (GPS) datasets, with a particular focus on anomaly detection. You will work with multiple complex data streams, including biometric data captured via wearable technologies and location-based datasets. This role will suit … technical direction in a fast-paced, evolving environment with minimal structure. Key Responsibilities Develop and deploy data science models, with a focus on anomaly detection Work across multiple datasets, including biometric and GPS/location data Analyse and interpret data generated from wearable hardware devices Collaborate with ...

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 ...

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 ...

/ELT pipelines, microservices and service mesh topologies. Champion AI, ML and agentic automation opportunities within the trading technology estate including intelligent scheduling, anomaly detection, automated P&L attribution, predictive maintenance and agentic workflow orchestration (e.g., AWS AgentCore). Define and govern DevOps, platform engineering and observability ...

analytics, model risk governance, and regulatory expectations for pricing/risk models. Hands-on ML/AI for quant finance (signal extraction, surrogate modeling, anomaly detection), including MLOps, drift monitoring, and explainability. Knowledge of portfolio optimization, hedging algorithms, execution analytics, and transaction cost modeling. Familiarity with distributed ...

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 ...

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 ...

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 ...

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

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. ...

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. ...

model training and downstream reporting. Apply supervised and unsupervised ML techniques to commercially relevant problems: usage propensity, client segmentation, churn modelling, content recommendation, and anomaly detection. Develop lightweight Python-based tools and notebook applications that allow business stakeholders to interact with model outputs or curated data extracts. Prototype ...

model training and downstream reporting.\ NApply supervised andunsupervised ML techniques to commercially relevant problems: usage propensity, client segmentation, churn modelling, content recommendation, and anomaly detection.\ NDevelop lightweight Python-based tools and notebook applications that allow business stakeholders to interact with model outputs or curated data extracts.\ NPrototype AI-assisted ...

model training and downstream reporting.\ NApply supervised andunsupervised ML techniques to commercially relevant problems: usage propensity, client segmentation, churn modelling, content recommendation, and anomaly detection.\ NDevelop lightweight Python-based tools and notebook applications that allow business stakeholders to interact with model outputs or curated data extracts.\ NPrototype AI-assisted ...

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 ...

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. ...

adherence to risk and control guidelines for data access and use.\ NData 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.\ NAutomation ...

management, BI, CDP/CRM, ad platforms, experimentation, consent/privacy, data warehouses) including build vs buy vs partner recommendations. Build proactive insight systems: anomaly detection, opportunity surfacing, segmentation, attribution-informed signals, and next best actions that guide teams to outcomes. Ensure Analytics actively powers other pillars ...

Threat Detection Engineer required for global legal firm. You will be responsible for advancing the maturity of the SIEM platform alongside other strategic security solutions, working closely with internal teams to improve the organisation's overall security posture. This position focuses on onboarding new log sources, optimising data … Engineering Design and implement detection use cases aligned to MITRE ATT&CK and threat intelligence Build and tune correlation rules, anomaly detections, dashboards, and alerting workflows Continuously refine detections to reduce false positives and improve effectiveness SOC & Incident Response Support Partner with SOC analysts ...

identify trends, patterns, and anomalies associated with potential ATO and Send Scam threats. Design and implement experiments to evaluate the effectiveness of fraud detection systems and continuously improve their performance. Collaborate with security analysts and engineers to translate business and security requirements into actionable data insights and solutions. … programming languages such as Python, R, or similar. Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL). Familiarity with anomaly detection, supervised, unsupervised learning methods, deep learning, and graph based solutions Demonstrated ability to work collaboratively in cross-functional teams and effectively ...

identify trends, patterns, and anomalies associated with potential ATO and Send Scam threats.\ NDesign and implementexperiments to evaluate the effectiveness of fraud detection systems and continuously improve their performance.\ NCollaborate with security analysts and engineers to translate business and security requirements into actionable data insights and solutions.\ NDevelop … Python, R, or similar.\ N\n Experience working with large datasets and data processing technologies (e.G., Hadoop, Spark, SQL).\ N\n Familiarity with anomaly detection, supervised, unsupervised learning methods, deep learning, and graph based solutions\ N\n Demonstrated ability to work collaboratively in cross-functional teams ...

trends, patterns, and anomalies associated with potential ATO and Send Scam threats. \n Design and implement experiments to evaluate the effectiveness of fraud detection systems and continuously improve their performance. \n Collaborate with security analysts and engineers to translate business and security requirements into actionable data insights … Python, R, or similar. \n \n Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL). \n \n Familiarity with anomaly detection, supervised, unsupervised learning methods, deep learning, and graph based solutions \n \n Demonstrated ability to work collaboratively in cross-functional teams ...

delivery in enterprise environments. Proven experience building and deploying end-to-end Machine Learning pipelines in production. Strong knowledge of ML (regression, classification, clustering, anomaly detection) and deep learning (PyTorch and/or TensorFlow). Hands-on experience with Generative AI, including LLMs, RAG systems, and agent ...