1 to 25 of 58 Permanent Anomaly Detection Jobs in London

improve threat detection, risk prioritisation and security decision-making. Key Responsibilities Develop machine learning and statistical models for threat detection, anomaly detection and security risk analytics. Design features and analytical methods using security telemetry, threat intelligence and enterprise data. Build and use Security … Strong hands-on Python and SQL skills. Experience with common ML/data science frameworks. Experience developing models for threat detection and anomaly detection . Experience with security risk analytics . Strong feature engineering experience. Experience working with cyber security data, logs or telemetry . ...

opportunities across AI-driven security and the security of AI systemsArchitect AI-powered security solutions spanning threat detection, SIEM/SOAR modernisation, anomaly detection, automated incident response, and predictive risk modellingDefine security architectures for AI pipelines, data platforms, model registries, and inference environments, embedding security … spanning security operations, cyber defence, and AI or cloud platform environmentsUnderstanding of AI/ML applications in security — threat detection, behavioural analytics, anomaly detection, and predictive risk modellingKnowledge of securing AI systems — including AI pipelines, MLOps practices, data platform security, model registries, and inference endpointsExperience ...

that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds. Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune … Domo, and Tableau specifically. Proficiency in Python or R for prototyping, and familiarity with common statistical and ML libraries and MLOps concepts. Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management. Familiarity with data product and data mesh operating models, domain-aligned ownership ...

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 ...

from AML, KYC, transaction monitoring, screening, CRM and other operational systems. AI and automation: Build reliable, well-governed datasets that support future machine learning, anomaly detection, alert optimisation and investigation automation. What I'm looking forThe most important requirement is someone with genuine technical depth across Snowflake … financial crime platforms, CRM systems and third-party vendor APIs. Data Vault, dimensional modelling or complex entity resolution. Building data foundations for machine learning, anomaly detection or GenAI applications. Experience with Azure Data Factory, Airflow or similar orchestration technologies. Why consider it? This is an opportunity ...

enabled automation, proactive support, and intelligent self-serviceMonitor and troubleshoot live integrations across MIF, REST and SOAP interfaces, and scheduled processesApply predictive monitoring and anomaly detection to identify emerging platform issues before they affect usersAI-Enabled Service Delivery and InnovationApply AI and automation to service operations, accelerating … runbooks, and automation frameworks for use across client engagementsIdentify and develop opportunities to extend client Maximo solutions with AI-enabled capability, including predictive maintenance, anomaly detection, and intelligent decision supportDocument and structure support knowledge, recurring patterns, and lessons learned into reusable assets that improve service quality ...

operational efficiency. Drive the practical adoption of AI and Machine Learning across media operations, including automated quality control, metadata enrichment, content segmentation, highlights generation, anomaly detection and predictive scheduling. Establish intelligent monitoring and operational resilience through centralised telemetry, observability, SLA reporting and self-healing workflows, improving service … playout, contribution and distribution. Practical experience applying AI/ML technologies to media operations, including automated QC, computer vision, speech-to-text, metadata enrichment, anomaly detection or intelligent workflow automation. A track record of taking complex technology solutions from concept and proof of concept through to reliable ...

resiliency strategy to gracefully manage failures in a large-scale distributed environment. Pioneer the development of an intelligent monitoring platform using ML to perform anomaly detection, predict job failures, and optimize grid performance. Engineer the secure and efficient data logistics required to move terabytes of market … event-driven architectures and handling asynchronous notifications and replies from distributed systems. Proven experience designing and building intelligent monitoring tools, ideally leveraging ML for anomaly detection and performance tuning. Expert-level experience managing workloads in a hybrid cloud environment, with deep knowledge of at least one major ...

What You'll DoData Engineering & Data Products & EnablementOwn the full lifecycle of business critical data pipelines: define SLAs, implement performance measurement, monitoring, alerting, and anomaly detection, and lead incident response and root-cause analysis. Define scalable, reusable data models and curated data sets for analytics, data science ...

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 ...

from development into production environments. Technical SkillsPython 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 ...

QualificationsExperience with pipelines and orchestration (e.g., Airflow, Prefect, Dagster, or similar) - strongly preferred. Practical experience applying AI tools to data work (coding assistance, investigation, anomaly detection).Experience supporting production data pipelines and incident workflows (monitoring, alerts, runbooks, operational readiness).Familiarity with modern data warehouses (e.g., Snowflake ...

reuse. Schedule, monitor, and manage data pipelines using Airflow or similar workflow orchestration tools to ensure reliable, timely data delivery. Implement validation, reconciliation, and anomaly-detection controls to maintain data completeness, accuracy, and consistency. Apply AI to automate schema inference across structured and semi-structured data sources ...

considerations. Familiarity with modern data infrastructure and architecture (APIs, pipelines, cloud platforms), with exposure to AI/ML or LLM-based enrichment solutions for anomaly detection and automation. Exposure to Agile methodologies such as Scrum or Kanban. Awareness of emerging trends in Private Markets data, including ...

/v4 Foundation or experience aligning QA with service management practices. Experience applying AI/ML in Quality Engineering, including test optimisation, defect prediction, anomaly detection or analytics-led assurance. Experience with enterprise orchestration tools such as Control-M, Autosys or equivalent scheduling platforms. Experience with master ...

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 ...

code, especially Java. Demonstrable experience collaborating with engineering on servicesExperience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL)Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time data analysisExperience with statistical analysis and good presentation skills to drive insight into … tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CDPrior experience in the fraud domain and a strong understanding of fraud detection techniquesExperience designing and deploying LLM-based solutions in productionWe're people without borders — without judgement or prejudice, too. We want to work with ...

data warehouse. Familiarity with data pipelines, APIs, workflow orchestration or system integrations. Exposure to governance, risk and compliance (GRC) platforms. Experience with statistical analysis, anomaly detection, forecasting or early-warning indicators. Experience using external regulatory, market or threat intelligence to support risk monitoring. Familiarity with AI agents ...

design it for the finance people who will use it. AI on the platform. This is where financial data turns into decisions: predictive forecasting, anomaly detection, AI-generated commentary and agentic workflows, with the human-in-the-loop controls that keep finance in charge of what ...

orchestration, retrieval-augmented generation (RAG), prompt engineering, and evaluation. Strong experience in the physical space: labs, manufacturing control, edge deployment of models, predictive and anomaly detection modeling, closed-loop control. Strong experience in lab automation and self-driving lab workflows: robotics, instrument integration, and digital-to-physical ...

scientific and computational approaches to advance flood hazard modeling capabilitiesExplore, prototype, and assess artificial intelligence and machine learning methods for surrogate modeling, spatial downscaling, anomaly detection, feature extraction, emulator development, and workflow automationDevelop and maintain reproducible scientific workflows, data processing pipelines, and code repositories using modern software ...

finance. AI-augmented, intelligent finance. You own the design of the AI and machine-learning use cases that make planning better — predictive forecasting, anomaly detection, automated variance analysis, AI-generated commentary and agentic planning workflows among them — and the human-in-the-loop controls that keep finance ...

huge impact to the security of the UK. Core Duties Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements ...

huge impact to the security of the UK. Core Duties Design and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applications Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirements ...

makes a huge impact to the security of the UK.Core DutiesDesign and develop machine learning models for traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM applicationsLead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly while adhering to governance requirementsTransition validated ...