1 to 25 of 63 Permanent Anomaly Detection Jobs in the UK excluding London

analyse integration pipeline telemetry and identify inefficiencies, bottlenecks, or redundant processes. Identify and Encourage Cost and Efficiency Savings AI Collaborate with DevOps to introduce anomaly detection models to proactively identify unusual patterns in deployment behaviour, system performance, or test failures. Collaborate with DevOps and QA teams … tools (Jira, Azure DevOps). AI Skills Experience with AI-driven test optimisation, intelligent prioritisation, and synthetic test data generation. Hands-on use of anomaly detection models in CI/CD pipelines. Comfortable with AI-powered telemetry, observability, and root-cause analysis tools. NICE-TO-HAVE Experience ...

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 ...

solve real-world problems. Expertise in statistical modelling, predictive analytics, clustering, classification, and regression techniques. Strong background in data mining, pattern recognition, and anomaly detection to improve service delivery. Proficient in Python, R, or other relevant programming languages used for data science. Strong skills ...

solve real-world problems. Expertise in statistical modelling, predictive analytics, clustering, classification, and regression techniques. Strong background in data mining, pattern recognition, and anomaly detection to improve service delivery. Proficient in Python, R, or other relevant programming languages used for data science. Strong skills ...

analysis. Work with the Quality team to automate FAI checks, SPC monitoring, supplier performance analysis, and NCR triage. Assist Production with digital work instructions, anomaly detection, and AI-supported training materials. Enterprise-level AI integration: Contribute to long-term AI architecture decisions choosing tools, shaping governance ...

business development. Mastery of enterprise architecture components, business/IT principles, and organisational processes. Experience applying AI/ML across corporate functions (e.g., financial anomaly detection, HR attrition prediction, procurement spend analytics). Competencies Exceptional analytical, planning, and organisational capabilities, with strong situational awareness and political acumen. ...

deep learning techniques to drive innovation in the FinTech domain. Hands-On Modelling & Delivery Build, train, validate, and deploy ML solutions across risk modelling, anomaly detection, personalisation, optimisation, and predictive analytics. Lead experimentation cycles, champion best practices, and ensure models are production-ready. Partner with Machine Learning ...

triaging and introduce automation for high-volume workflows Design and build Gen AI LLM/VLM solutions for self driving car behavior analysis and anomaly detection Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a Reinforcement Learning from human preference ...

hands-on R&D role involves analysing complex RF datasets, developing advanced signal-processing pipelines, and contributing to cutting-edge UAV/drone detection technologies. You will play a key role in prototyping new sensing capabilities, working with SDRs, designing real-world RF experiments, and integrating machine-learning … front-end behaviour (filters, mixers, LOs, AGC, noise figure) to improve signal integrity and inference accuracy Developing ML and statistical models for RF classification, anomaly detection, and emitter identification Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration ...

hands-on R&D role involves analysing complex RF datasets, developing advanced signal-processing pipelines, and contributing to cutting-edge UAV/drone detection technologies. You will play a key role in prototyping new sensing capabilities, working with SDRs, designing real-world RF experiments, and integrating machine-learning … front-end behaviour (filters, mixers, LOs, AGC, noise figure) to improve signal integrity and inference accuracy Developing ML and statistical models for RF classification, anomaly detection, and emitter identification Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration ...

role ideally suited to engineers and scientists who enjoy fast-paced prototyping, complex problem-solving, and developing cutting-edge UAV/drone detection technologies. In this role, you will analyse complex RF data from software-defined radios (SDRs), develop advanced signal-processing pipelines, and contribute directly … jitter. You will also model RF front-end behaviour, improving signal integrity and inference accuracy, and apply machine learning and statistical models for classification, anomaly detection, and emitter identification. You will prototype real-time and batch-processing systems using Python (NumPy, SciPy, PyTorch) and integrate them with ...

streams which could range from supporting the build out of our threat hunting and operations capabilities, improving our playbooks and breach response, to Cloud anomaly detection and response. What You'll Be Doing Enhance our Security Operations capabilities and abilities to threat hunt Work closely with other ...

streams which could range from supporting the build out of our threat hunting and operations capabilities, improving our playbooks and breach response, to Cloud anomaly detection and response. What You'll Be Doing Enhance our Security Operations capabilities and abilities to threat hunt Work closely with other ...

streams which could range from supporting the build out of our threat hunting and operations capabilities, improving our playbooks and breach response, to Cloud anomaly detection and response. What You'll Be Doing Enhance our Security Operations capabilities and abilities to threat hunt Work closely with other ...

scalable and robust product architecture. Define and evolve chart-of-accounts mapping, ensuring alignment with industry-standard frameworks where applicable. Extend error- and outlier-detection logic, annualisation processes and export capabilities (Excel, API and additional formats as required). Partner with customers and internal stakeholders to understand pain ...

cloud governance and FinOps tools to improve cost visibility and control Forecasting & Budgeting – Produce accurate cost projections and align cloud spend with financial planning Anomaly Detection – Identify and respond to cost anomalies to prevent unexpected budget spikes Reporting & Documentation – Prepare clear and comprehensive reports on cloud financial ...

safety tools (e.g., Responsible AI Toolbox, Deepchecks) Familiarity with Azure AI Foundry and LLM compliance testing frameworks Prompt injection testing and AI behaviour anomaly detection This is a great role, and an awesome time to join this business as they expand their artificial intelligence team. For more ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...