26 to 50 of 111 Anomaly Detection Jobs in London

data pipelines’ SLA and dependency management.Writing technical documentation for data solutions, and presenting at design reviewsSolving data pipeline failure events and implementing sound anomaly detection Working with various teams from analytics to product owners and front end developers on tracking solutions and solving technical challengesLeading medium ...

between regulatory intent and technical execution. In this role, you will serve as the global second-line authority and owner of financial crime detection models, risk analytics, and automated control frameworks. You will own the global methodology, thresholds, and data-scoring models used to identify and mitigate financial … native, API-first environment where transaction decisions must be executed algorithmically in milliseconds.Proven track record of moving advanced machine learning, neural networks, or behavioral anomaly detection models out of testing and into live, global compliance operations safely and transparently.Experience utilizing graph databases and link analysis to identify ...

preventative controls. Drive adoption of automation-first principles, reducing dependency on human-driven processes. Contribute to AI-driven operational use cases, including event correlation, anomaly detection, noise reduction, operational insights, and automated remediation. Ensure AIOps capabilities are grounded in reliable telemetry, clear control boundaries, and measurable operational … Operational Excellence Define standards for telemetry, monitoring, alerting, and operational visibility across all critical systems. Ensure services are observable, measurable, and support proactive detection of issues. Improve operational readiness, incident response effectiveness, and time-to-recovery through engineering solutions. CI/CD & Platform Integration Contribute to the design ...

champion within Corporate FP&A, defining and executing the AI roadmap for planning, forecasting, reporting and process automation.Embed AI-enabled forecasting, anomaly detection, driver-based scenario modelling and natural language query capabilities into the EPM and reporting stack.Partner with the CIO, Data & AI teams and Finance leadership ...

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. ...

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. ...

Lambda, Aurora, Airflow, Terraform). Ensure data availability, integrity, and performance as the platform scales globally. Drive best practices in data quality, automated testing, anomaly detection, and CI/CD. Partner with Product, Engineering, and Commercial teams to translate data needs into platform capabilities. EHR Integration ...

Monitor adherence to risk and control guidelines for data access and use.Data quality, reliability, and lineage - Define data contracts for telemetry sources; implement validation, anomaly detection, and reconciliation; document definitions (e.g., formulas, thresholds) and end-to-end data lineage from raw signals to KPIs and insights.Automation ...

including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines. Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI‐assisted cloud operations. Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI‐enabled workloads. Google ...

MLOps, including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines.Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI-assisted cloud operations.Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI-enabled workloads.Google Cloud certifications ...

telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails and security controls (SCPs/IAM boundaries, OPA policies ...

AIOps: Translate NFRs into pragmatic architecture decisions, define SLOs/SLIs, and design modern observability (metrics, logs, traces). Apply AIOps for alert reduction, anomaly detection, incident triage, and continuous improvement across hybrid and cloud platforms. Advisory & communication: Simplify complexity for senior audiences; align diverse viewpoints ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

resiliency, cyber risk reduction, and operational efficiency.Serve as the executive sponsor for AI-enabled operational transformation initiatives.Autonomous Operations TransformationImplement AI-driven monitoring, event correlation, anomaly detection, and root-cause analysis capabilities.Lead the deployment of autonomous operational workflows capable of identifying, diagnosing, and resolving incidents without human intervention.Establish ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance frameworks ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance frameworks ...

performance intelligence, and financial reconciliation across all travel verticals.Architect and evolve reference pricing systems leveraging predictive modeling, elasticity signals, competitive benchmarking, and real-time anomaly detection.Build the supplier intelligence layer, transforming transactional data into automated performance signals, demand insights, and pricing diagnostics.Establish and govern the canonical source of truth … framing, signal design, evaluation metrics (e.g., precision/recall tradeoffs, forecasting accuracy), monitoring frameworks, drift detection, and retraining strategies.Experience shipping predictive systems, anomaly detection platforms, automated decisioning infrastructure, or model-driven pricing systems at scale.Experience owning pricing systems, financial reporting platforms or marketplace intelligence tools.Deep ...

intelligence, and financial reconciliation across all travel verticals. Architect and evolve reference pricing systems leveraging predictive modeling, elasticity signals, competitive benchmarking, and real-time anomaly detection. Build the supplier intelligence layer , transforming transactional data into automated performance signals, demand insights, and pricing diagnostics. Establish and govern the canonical source … signal design, evaluation metrics (e.g., precision/recall tradeoffs, forecasting accuracy), monitoring frameworks, drift detection, and retraining strategies. Experience shipping predictive systems, anomaly detection platforms, automated decisioning infrastructure, or model-driven pricing systems at scale. Experience owning pricing systems, financial reporting platforms or marketplace intelligence ...

feedback, surfacing systemic issues, and influencing platform enhancements via the Services Observability Forum.* Stay current with observability trends, including AI/ML-driven insights, anomaly detection, and emerging OSS practices, and assess their applicability.* Maintain strong knowledge of observability platform features and vendor offerings to advise teams ...

tooling feedback, surfacing systemic issues, and influencing platform enhancements via the Services Observability Forum.Stay current with observability trends, including AI/ML-driven insights, anomaly detection, and emerging OSS practices, and assess their applicability.Maintain strong knowledge of observability platform features and vendor offerings to advise teams ...

AIOps: Translate NFRs into pragmatic architecture decisions, define SLOs/SLIs, and design modern observability (metrics, logs, traces). Apply AIOps for alert reduction, anomaly detection, incident triage, and continuous improvement across hybrid and cloud platforms.Advisory & communication: Simplify complexity for senior audiences; align diverse viewpoints; and negotiate ...

technical demos that had to stand up in front of real users.Experience packaging desktop applications or supporting Windows-heavy enterprise environments.Exposure to forecasting, anomaly detection, optimization, time-series systems, or other decision-support workflows.Experience in energy, commodities, financial, trading, market operations, or industrial workflows.Team and environmentQuantAI sits ...

clear judgment on when AI is appropriate versus deterministic or rules-based approaches. Strong understanding of analytics and AI in production (e.g. data quality, anomaly detection, fraud/AML, workflow automation) and understands what it takes to run these reliably in regulated environments. Designs with security ...

technology teams to optimise semantic models for AI readiness.Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection.Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership.Establish best practices, standards, and governance frameworks for responsible ...

Design & Data Architecture Identify key signals required for: Quality & reliability Latency & throughput Cost tracking & optimisation MCP usage patterns User workflow insights Failure pattern detection Guardrail and safety event monitoring Define retention rules … considerations, anonymisation and usage policies in partnership with AI Governance and Compliance. Platform & Tooling Own requirements for dashboards, monitoring tools, model comparison views, anomaly detection alerts, and performance scorecards. Design systems that allow product and engineering teams to self-serve insights about AI model behavior ...