26 to 50 of 208 Anomaly Detection Jobs in the UK

Experience in insurance or other financial services. Experience with B2B, enterprise software, or complex customer implementations. Interest or experience in fraud detection, anomaly detection or other risk-related domains. Familiarity with agile product development methods and tools such as Jira, Confluence or equivalent. Qualifications ...

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 ...

Phase 1 AIOps use cases. Configure and optimize: Event Management Metric Intelligence Health Log Analytics Event correlation Impact analysis CMDB Service Mapping Configure anomaly detection for: CPU utilization Memory utilization Disk utilization Support root cause analysis workflows. Validate monitoring integrations and event sources. Configure service dependency … visibility. Support event-noise reduction initiatives. Participate in testing, validation, and outcome demonstrations. Why a TC is Required The agreed technical scope includes: Infrastructure anomaly detection Event correlation Metric Intelligence tuning HLA implementation Network impact visibility Service dependency analysis Root cause analysis demonstrations #J-18808-Ljbffr ...

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 ...

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 ...

meet evolving analytical and business requirements. Drive high standards of data quality, integrity and governance through the implementation of automated validation, cleansing, monitoring and anomaly detection processes. Integrate, transform and harmonise data from a wide range of internal and external sources, including policy, claims, telematics and third ...

from development into production environments. 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 ...

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. ...

generation, fine-tuning, or prompt engineering Exposure to distributed data processing frameworks, such as Spark Exposure to fraud detection, anti-money laundering, anomaly detection, or graph and network-based modelling Awareness of model risk management, governance, and regulatory controls within financial services ...

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 ...

real-world security challenges. The role Develop statistical models and unsupervised learning techniques to identify anomalies in complex datasets. Investigate data, evaluate different detection approaches and assess their effectiveness. Turn experimental work into robust, tested and maintainable production software. Contribute to architecture and technical decisions as the product … writing reliable, well-tested production code. Strong Python engineers with previous Java experience and solid engineering fundamentals will also be considered. Experience in cybersecurity, anomaly detection, fraud detection or behavioural analytics would be valuable, alongside exposure to ClickHouse or numerical and ML libraries. What ...

bridging operational systems and the analytical layer until IT takes full ownership. Develop and implement AI/ML-enabled data features — including data enrichment, anomaly detection, and predictive pipelines. Integrate AI APIs and LLM-based tools into data workflows to accelerate analytics at scale. Work closely with ...

services. Establish self-service data quality capabilities, monitoring frameworks, observability solutions, and quality controls across critical enterprise data assets Enable proactive data profiling, anomaly detection, root cause analysis, and issue management capabilities. Partner with Data Engineering and Data Product teams to embed quality controls throughout the data ...

collaboration within multidisciplinary teams, supported by strong problem-s 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 ...

services, banking, wealth management, aviation, telecoms, public sector, or other regulated industries. Experience with enterprise document intelligence, knowledge graph systems, forecasting platforms, threat intelligence, anomaly detection, or AI-assisted decision systems. Background in designing reusable AI reference architectures and platform patterns. Why This Role This ...

data cataloguing, master data management and enterprise data-quality frameworks. Machine learning and advanced analytics: Experience applying analytical or AI techniques to data quality, anomaly detection or operational automation Cost Optimization: Understanding of Fabric capacity management and cost-efficient solution design. WHAT SUCCESS LOOKS LIKE Synapse workloads ...

decision-making. You will develop advanced analytical use cases across areas such as forecasting, revenue leakage, client churn, sales conversion, profitability, capacity and anomaly detection. You will balance analytical rigour with speed, keeping solutions understandable, actionable and focused on measurable business outcomes. You will work closely with Data Engineering ...

libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...

libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...

security of AI (protecting the organisation's own AI systems and use of AI) and AI for security (using AI to strengthen protection, detection and response). This role will sit within Cyber Security and Compliance. Technical ownership of AI security tool selection and deployment Define requirements … with stakeholders. In protection, that might mean AI-assisted vulnerability prioritisation, secure code review, or posture analysis. In detection, it covers behavioural anomaly detection, alert triage and enrichment, and LLM-assisted threat hunting. In response, it includes AI copilots for investigation and incident summarisation, automated ...

security of AI (protecting the organisation's own AI systems and use of AI) and AI for security (using AI to strengthen protection, detection and response). This role will sit within Cyber Security and Compliance. Technical ownership of AI security tool selection and deployment Define requirements … with stakeholders. In protection, that might mean AI-assisted vulnerability prioritisation, secure code review, or posture analysis. In detection, it covers behavioural anomaly detection, alert triage and enrichment, and LLM-assisted threat hunting. In response, it includes AI copilots for investigation and incident summarisation, automated ...

needs to act early Apply statistical and machine‐learning techniques to spot unusual data patterns, including issues that may otherwise go unnoticed Automate detection, alerting and safe recovery of common issues, and give the wider team the tools to investigate and fix problems themselves Trace how data … with Cyber Security and Infrastructure data, such as ServiceNow, Entra ID and Active Directory Applying machine learning or AI to operational problems, such as anomaly detection AI‐assisted development with tools such as GitHub Copilot and Claude Code Streamlit and Power BI A Snowflake or architecture certification ...