26 to 50 of 123 Anomaly Detection Jobs in London

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 ...

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 ...

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 ...

data structures that abstract technical complexity from business users. Help evolve the practice from static report production toward AI-augmented insight, such as automated anomaly detection, forecasting, and plain-language explanation of results. Enforce review and validation standards for AI-assisted development and AI-generated insights ...

models, ensuring business definitions and relationships are consistently represented in the engineered data products. Apply AI across data engineering, from data quality monitoring and anomaly detection to automated documentation and AI-assisted code, with appropriate human oversight. Simplify the estate and retire legacy platforms: lead the technical ...

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 ...

especially Java. Demonstrable experience collaborating with engineering on services Experience 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 analysis Experience with statistical analysis and good presentation skills to drive insight … Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection techniques Experience designing and deploying LLM-based solutions in production We’re people without borders — without judgement or prejudice, too. We want to work ...

especially Java. Demonstrable experience collaborating with engineering on services Experience 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 analysis Experience with statistical analysis and good presentation skills to drive insight … Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection techniques Experience designing and deploying LLM-based solutions in production We’re people without borders — without judgement or prejudice, too. We want to work ...

churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning. Optimization: Building engines for marketing spend, inventory management, or resource allocation. Causal ...

churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions. Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning. Optimization: Building engines for marketing spend, inventory management, or resource allocation. Causal ...

incident, backup, resilience and monitoring controls. Practical experience applying AI, Agentic AI, AIOps or intelligent automation to operational use cases such as incident triage, anomaly detection, runbook automation, knowledge search, root cause support or service desk workflows. Knowledge of observability and reliability practices, including SRE principles, service ...

OpenTelemetry traces, change events, and runbooks to accelerate Mean Time to Detection (MTTD) and Resolution (MTTR). Safe Production Automation: Develop proactive anomaly detection and Human-in-the-Loop (HITL) remediation workflows with rigorous safety, security, and quality guardrails. Reliability & Scalability Engineering: Partner with application ...

graph and putting it to work in an enterprise. Machine learning and data science — building models that solve real business problems, such as forecasting, anomaly detection or classification. FDE, presales or innovative professional services — experience on a team that builds with customers. Technical consulting — designing and building ...

graph and putting it to work in an enterprise. Machine learning and data science — building models that solve real business problems, such as forecasting, anomaly detection or classification. Experience in our industries — manufacturing, engineering, supply chain, pharma, aerospace or financial services. Travel Some travel is expected ...

reporting tools to city and transit operators. AI/ML Readiness: Monitor and evaluate practical AI use cases (e.g., predictive maintenance for hardware, anomaly detection) to be integrated into the roadmap, value-add features once the core BI and Data Lake foundations are fully established. Data Governance ...

software development lifecycle (e.g., automated code reviews, synthesis, workflow orchestration, agent-driven monitoring/troubleshooting). Prototype and evaluate agentic workflows for automated anomaly detection, market data quality validation, and intelligent developer tooling. Technical Leadership & Excellence Drive engineering best practices: test-driven development (TDD), CI/ ...

applied within service management to improve operational efficiency, employee experience and service outcomes.* Awareness of modern AIOps and observability capabilities, including monitoring, event correlation, anomaly detection, intelligent alerting, operational analytics and automation.* Familiarity with enterprise service management platforms such as ServiceNow, Jira Service Management, Freshworks or similar ...

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 ...

tasks. AIOps & Intelligent Observability: Ability to integrate traditional observability stacks (e.g., Datadog, Prometheus, OpenTelemetry) with AI/ML models to automate root-cause analysis, anomaly detection, and semantic log clustering. Self-Healing Infrastructure Engineering: Experience designing closed-loop, self-healing systems that autonomously execute recovery actions (e.g. ...

before systems are decommissioned. Build or coordinate data pipelines and ETL processes; apply AI and machine learning tooling to accelerate data ingestion, deduplication, and anomaly detection. Design automated quality checks that reduce manual validation effort without compromising accuracy. Master Data Management & Governance Apply MDM principles to align acquired data ...

improvement. Job Responsibilities: Develop and implement advanced machine learning models and algorithms to solve operational challenges Deploy AI/ML solutions for process automation, anomaly detection, document intelligence, and workflow optimization Architect and oversee generative AI applications to automate and enhance business processes Build and maintain production ...

such as planning, documentation, and evidence review. Predictive Analytics & Machine Learning Experiment with ML algorithms (e.g., K-Means, Decision Trees, Isolation Forest, ARIMA) for anomaly detection, risk assessment, and forecasting. Collaborate with technical teams to integrate predictive models into audit processes. Audit Integration & Enablement Work alongside audit ...

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