1 to 25 of 136 Permanent Anomaly Detection Jobs in London

deploy machine learning models that solve real-world financial crime and risk challenges. Work on problems such as fraud detection, chargeback prediction, anomaly detection, identity verification, and transaction monitoring. Transform ambiguous business problems into measurable ML solutions. Partner closely with Product and Operations teams … deploy machine learning models that solve real-world financial crime and risk challenges. Work on problems such as fraud detection, chargeback prediction, anomaly detection, identity verification, and transaction monitoring. Transform ambiguous business problems into measurable ML solutions. Partner closely with Product and Operations teams ...

samples, and you'll set Nominal's direction on ML for hardware data, from classical methods to deep learning. What you'll do Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry. Package these methods as agent tools … bring 8+ years in applied ML or statistics, with production systems on time-series or sensor data. Deep classical grounding: statistics, signal processing, anomaly detection, and forecasting. Hands-on deep learning: transformers, embeddings, and representation learning for sequences and sensor data. Strong software engineering; your methods ship ...

work will help reduce manual effort and enable services to deliver accurate, timely customer migrations Key Responsibilities: Apply classification, regression, ranking, forecasting, clustering, and anomaly detection to enterprise AI and agentic use cases. Develop and evaluate intent classification, semantic matching, agent/skill/tool ranking … embeddings, and LLM-based baselines. Use statistical modeling to estimate execution latency, token demand, inference costs, workload trends, and resource requirements. Apply clustering and anomaly detection to identify recurring intents, capability gaps, unusual agent behavior, and execution regressions. Conduct exploratory analysis, dataset preparation, feature engineering, hypothesis testing ...

model deployment, contributing hands‐on where your expertise accelerates delivery. Support the responsible use of AI within infrastructure operations, monitoring triage, documentation generation, anomaly detection in line with JTC’s AI governance framework. I NFRASTRUCTURE AUTOMATION & SECURITYENGINEERING Automation is how we scale without proportionally scaling headcount. Security … policy-as-code (Open Policy Agent or equivalent), secrets management, dependency scanning and automated compliance evidence generation. Implement FinOps discipline: tagging strategies, cost allocation, anomaly detection and rightsizing programmes to ensure cloud spend is transparent, attributed and justified. Automate routine operational activity, patching, health checks, user lifecycle ...

Security is a domain where AI genuinely matters. The attackers are using it, and the defenders need to as well. You'll work on anomaly detection, behavioural analysis, natural language processing for threat intelligence, and building interfaces that help analysts respond faster. The problems are adversarial … noisy, and the stakes are real. Responsibilities Build analyst-facing interfaces for investigating and responding to security threats Design and implement ML models for anomaly detection, behavioural analysis, and threat classification Create data pipelines that ingest and process security telemetry at massive scale Build APIs and backend ...

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

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 ...

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 ...

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 for The most important requirement is someone with genuine technical depth across … 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 ...

contracts, capabilities, consumption patterns and priorities for internal and external use cases. Identify opportunities to leverage AI and machine learning to improve data classification, anomaly detection, validation, conflict resolution and other data‐intensive workflows. Work with Data Operations to move towards an exception‐based operating model, where … real‐time or near‐real‐time data processing. Experience applying AI/ML to geospatial or data‐quality problems, such as feature extraction, classification, anomaly detection or automated validation. Experience building developer‐facing products, APIs, SDKs or data services. Experience taking a platform from early‐stage architecture ...

merchant fraud, abuse, chargebacks, and anomalous behaviour. Lead the adoption of modern modelling techniques, including: Transformers and sequence models Representation learning & embeddings Autoencoders and anomaly detection Graph/network-based models Semi-supervised and weakly supervised learning Own the end-to-end model lifecycle: problem framing, feature … Technical Skills: Expert-level Python and modern ML tooling. Strong hands-on experience with: Transformers, LSTM/sequence models Representation learning & embeddings Autoencoders & anomaly detection Experience operationalising models on AWS SageMaker (or equivalent). Strong understanding of model evaluation, explainability, and monitoring in high-risk environments. What ...

proactive support, and intelligent self-service Monitor and troubleshoot live integrations across MIF, REST and SOAP interfaces, and scheduled processes Apply predictive monitoring and anomaly detection to identify emerging platform issues before they affect users AI-Enabled Service Delivery and Innovation Apply AI and automation to service … automation frameworks for use across client engagements Identify and develop opportunities to extend client Maximo solutions with AI-enabled capability, including predictive maintenance, anomaly detection, and intelligent decision support Document and structure support knowledge, recurring patterns, and lessons learned into reusable assets that improve service quality ...

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 … 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 ...

with engineers, analysts, and commercial stakeholders, you will translate ambiguous business problems into precise technical solutions and bring novel approaches such as graph networks, anomaly detection, and behavioural signals into production where they create real impact.**Who you are*** End-to-end ML ownership across the full … systems in early-stage startups or small greenfield teams. This is a strong positive signal.* Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning.* Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices.* Experience mentoring ...

into end-to-end monitoring solutions. Maintain strong knowledge of observability platforms and vendor offerings and stay current with AI/ML-driven insights, anomaly detection, and emerging practices, assessing their applicability to Marex. Incident command & stakeholder management: Closely monitor relevant Jira queues and ensure tickets … environments. Excellent understanding of the process and workflow from Development, UAT, and deployment to Production. Familiarity with AI/ML‐driven triage, event correlation, anomaly detection, and alert‐tuning capabilities, and their practical application to reducing operational toil. Proven ability to troubleshoot integration issues and support observability ...

deliver accurate work within agreed timescales Proactive mindset and enthusiasm for continuous learning Preferred Skills Experience using AWS Cost Explorer, AWS Budgets, or Cost Anomaly Detection Familiarity with AWS Savings Plans and Reserved Instances Experience producing technical, financial, or operational reports and dashboards Knowledge of AWS tagging … reporting tools AWS Cloud Practitioner, AWS Associate, or FinOps Certified Practitioner certification AWS Services EC2 RDS S3 AWS Cost Explorer AWS Budgets AWS Cost Anomaly Detection Savings Plans Reserved Instances Reporting & Analytics Excel Power BI QuickSight Dashboards Technical, financial, and operational reporting FinOps Responsibilities Produce cost ...

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 … 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 ...

resolution Encode triage logic into automation pipelines so future instances are classified and escalated without manual intervention Apply ML and AI techniques — crash clustering, anomaly detection, pattern recognition — to build smarter triage pipelines Build debug tooling, scripts, and CI/CD integrations that surface OS-level signals … Exposure to automation frameworks, CI/CD pipelines, or scalable test systems Experience applying ML or AI techniques to systems problems — crash clustering, log anomaly detection, failure classification, or intelligent alert prioritization Familiarity with LLMs or generative AI tooling in an engineering context — prompt engineering, RAG pipelines ...

conversational interfaces, AI-powered agents, and developer experience tools. Your scope covers everything from in-chat interactions and agent-driven workflows to proactive notifications, anomaly detection, and human-in-the-loop interventions across payroll, HR, and compliance.This isn’t just about making AI features look good … Akai, conversational interfaces, agents, labs, and developer experience tools.Establish the strategic approach for how intelligence surfaces across the platform—from proactive agent notifications and anomaly detection to in-chat actions and workflow automation.Shape strategic conversations using insights, narratives, and design vision to influence direction and uncover ...

over another, so it reasons its way to the right answer on unfamiliar jobs. Run the model lifecycle as one continuous process: data curation, anomaly detection, training and calibration, evaluation, generalisation and feedback loops. Own the architecture of the vertical AI and how it works with … able to spend regular days in London. Nice to have Specialist logistics experience from the inside; logistics tech, freight or pricing systems; anomaly detection on operational data; working across models from more than one provider; early-stage start-up experience. What we offer Share options: Meaningful share ...

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 ...

Enterprise Solutions, shaping high-rigor analytical capabilities across a complex, regulated global financial-services environment. Design, build, and operate production-grade models for anomaly detection, variance analysis, drift detection, forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. ...

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 Executives (CTO, CFO, CIO) — Present cost visibility dashboards, business cases for optimisation, and FinOps maturity roadmaps ...

Effective collaboration within multidisciplinary teams, supported by strong problem-solving, documentation and critical-thinking skills.**Desirable Skills & Experience*** Advanced statistical modelling, experimental design, forecasting, anomaly detection, model optimisation or explainability.* Deployment and operation of machine-learning models, including monitoring, drift detection and performance management.* Awareness ...