1 to 25 of 81 Remote/Hybrid Anomaly Detection Jobs in the UK

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 ...

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 ...

toxic combinations, identity exposure, control gaps, and vulnerability prioritization. Build and evaluate graph algorithms and ML models including centrality, community detection, similarity, anomaly detection, node classification, link prediction, embeddings, and Graph Neural Networks. Design GraphRAG and knowledge-grounded security assistants that combine graph traversal, vector … JanusGraph . Graph data science: Neo4j Graph Data Science, NetworkX , PyTorch Geometric or DGL; graph embeddings, pathfinding, similarity, clustering, link prediction, node classification, anomaly detection, and GNN development. Programming and engineering: Advanced Python and SQL; APIs; test automation; data structures; distributed processing; Git; CI/CD; containers ...

Collaborate with engineering and business stakeholders to identify performance trends, operational opportunities, and actionable insights. Create data pipelines and workflows that support operational monitoring, anomaly detection, service analytics, and business intelligence initiatives. Integrate data from multiple enterprise systems including Azure, SQL databases, APIs, cloud platforms, and operational … Azure Data Factory, Synapse Analytics, Data Lake, or related cloud data technologies. Experience creating executive dashboards and operational reporting solutions. Knowledge of machine learning, anomaly detection, predictive analytics, or statistical analysis techniques. Experience working with large-scale telemetry, operational, or IoT datasets. Familiarity with DevOps practices ...

Collaborate with engineering and business stakeholders to identify performance trends, operational opportunities, and actionable insights. Create data pipelines and workflows that support operational monitoring, anomaly detection, service analytics, and business intelligence initiatives. Integrate data from multiple enterprise systems including Azure, SQL databases, APIs, cloud platforms, and operational … Azure Data Factory, Synapse Analytics, Data Lake, or related cloud data technologies. Experience creating executive dashboards and operational reporting solutions. Knowledge of machine learning, anomaly detection, predictive analytics, or statistical analysis techniques. Experience working with large-scale telemetry, operational, or IoT datasets. Familiarity with DevOps practices ...

Centre of Excellence for Conversational Analytics, shape product direction for conversational analytical use cases and prototype new AI capabilities such as analytical agents, anomaly detection and process intelligence. You will work across business, data, engineering, governance and risk teams to embed solutions safely and effectively across … development, helping to shape, prototype and evaluate analytical agents, intelligent workflows and AI-enabled products. This will involve exploring opportunities in areas such as anomaly detection, process intelligence, root‐cause analysis, decision support and automated insight generation, turning ambiguous business challenges into testable hypotheses, rapid prototypes ...

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 ...

should expect to join customers periodically and for technical discussions. Engineers who never meet the customer build the wrong thing. KEY RESPONSIBILITIES Take our anomaly detection work from proof of concept into production across the estate, moving from retrospective analysis to live early warning. Build the validation … stack. Hands‐on experience with time‐series, sensor or telemetry data at scale, including the realities of gaps, drift, flatlines and misbehaving devices. Applied anomaly detection or predictive modelling taken into production, with the judgement to keep methods robust and explainable rather than elaborate. Practical experience building ...

this role you will define and deliver next-generation finance capabilities that include predictive modelling, trend analysis, risk identification, automated insight generation, and faster anomaly detection across the business, Working closely with Finance, Accounting, Product and Technology stakeholders, you will translate complex business challenges into scalable digital … Define and own the product vision, roadmap, and success measures for AI-enabled finance technology products across financial management, predictive modelling, insight generation, and anomaly detection. Partner with Finance, Accounting, Product and Technology teams to identify high-value business problems and convert them into prioritised product outcomes, requirements ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...