1 to 25 of 140 Permanent Anomaly Detection Jobs in London

production deployment before a model earns trust at scale. This is a second-line position, reviewing methodologies built with scikit-learn, LightGBM, graph models, anomaly detection, and increasingly GenAI-based components. You'll also build your own tooling — agentic AI systems that read model documentation and code … regulatory expectations on model risk, fairness, and data privacy. You'll validate emerging techniques as first-line teams adopt them — graph networks, behavioral biometrics, anomaly detection, and GenAI-based systems. You'll document validation outcomes and communicate model risks directly to first-line data scientists, ML engineers ...

Security is a domain where AI genuinely matters. The attackers are using it, and the defenders need to as well. You'll work on anomaly detection, behavioural analysis, natural language processing for threat intelligence, and building interfaces that help analysts respond faster. The problems are adversarial … noisy, and the stakes are real. Responsibilities Build analyst-facing interfaces for investigating and responding to security threats Design and implement ML models for anomaly detection, behavioural analysis, and threat classification Create data pipelines that ingest and process security telemetry at massive scale Build APIs and backend ...

with many different collection techniques, along with internal QA tools. This role will specialise in taking our QA systems further: hardening the validation and anomaly detection that catches problems before our customers do, keeping our scrapers healthy and resilient as sources change under us, and shipping fast … puzzle and wants to own and raise an existing quality bar rather than maintain someone else's. Design and build data quality checks - validation, anomaly detection, freshness and coverage monitoring - across our core data pipelines Own and improve production web scraping infrastructure (Playwright, proxy management, anti ...

that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds. Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune … Domo, and Tableau specifically. Proficiency in Python or R for prototyping, and familiarity with common statistical and ML libraries and MLOps concepts. Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management. Familiarity with data product and data mesh operating models, domain-aligned ownership ...

stories, acceptance criteria, and prioritized backlog items across the AIOps product lifecycle. Ownership of high-impact AIOps capabilities, including noise reduction, event management, correlation, anomaly detection, root cause analysis, predictive alerting, and self-healing remediation. Partner across Operations, infrastructure, SRE, platform engineering, service management, application teams, security … experience in product management, IT operations, SRE, observability, platform engineering, or related enterprise technology roles. Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation. Experience defining product roadmaps, managing requirements and backlog priorities, and delivering technical ...

stories, acceptance criteria, and prioritized backlog items across the AIOps product lifecycle. Ownership of high-impact AIOps capabilities, including noise reduction, event management, correlation, anomaly detection, root cause analysis, predictive alerting, and self-healing remediation. Partner across Operations, infrastructure, SRE, platform engineering, service management, application teams, security … experience in product management, IT operations, SRE, observability, platform engineering, or related enterprise technology roles. Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation. Experience defining product roadmaps, managing requirements and backlog priorities, and delivering technical ...

high-quality inputs for models Designs, builds and trains data science products such as business models, predictive analytics tools, recommender systems, natural language processing, anomaly detection, forecasts, optimisation models and decision support systems Works with Digital teams to productionise models and deliver application front ends Translates results … high-quality inputs for models Designs, builds and trains data science products such as business models, predictive analytics tools, recommender systems, natural language processing, anomaly detection, forecasts, optimisation models and decision support systems Works with Digital teams to productionise models and deliver application front ends Translates results ...

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 ...

Proof‐of‐Concept to production‐grade, fleet‐level deployments. Preferred Experience working on GPU fleet management, hyperscale infrastructure, or AI training clusters. Experience building anomaly detection or failure prediction systems for hardware or distributed systems. Experience implementing distributed straggler detection or collective‐level performance analysis … curious about distributed systems failure modes, GPU performance pathologies, and reliability engineering at scale. You’re excited by the idea of moving from anomaly detection to prediction to autonomous root cause reasoning. You enjoy designing platforms that protect uptime, revenue, and customer trust through proactive systems thinking. ...

unstructured commercial and operational shipping data such as customs filings, fixtures, and SPAs. These inputs drive predictive systems that support energy-demand forecasting, anomaly detection, and real-time recommendations for physical and derivative trading. What You'll Be Doing As a Principal Data Scientist, reporting toVortexa … depth ofexpertiserather than the credential. Fluent in Python and broad in your modelling toolkit: withstrongexperience across regression and classification, clustering, time-series analysis, anomaly detection, sequence-to-sequencearchitecturesand stochasticoptimisation. Comfortable withsolvingambiguous problems:able to start with an abstract question, interrogate it, decide what is worth solving, choose ...

unstructured commercial and operational shipping data such as customs filings, fixtures, and SPAs. These inputs drive predictive systems that support energy-demand forecasting, anomaly detection, and real-time recommendations for physical and derivative trading. What You'll Be Doing As a Staff/Principal Data Scientist, reporting … rather than the credential. Fluent in Python and broad in your modelling toolkit: with strong experience across regression and classification, clustering, time-series analysis, anomaly detection, sequence-to-sequence architectures and stochastic optimisation. Comfortable with solving ambiguous problems: able to start with an abstract question, interrogate ...

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 ...

with engineers, analysts, and commercial stakeholders, you will translate ambiguous business problems into precise technical solutions and bring novel approaches such as graph networks, anomaly detection, and behavioural signals into production where they create real impact. Who you are End-to-end ML ownership across the full … systems in early-stage startups or small greenfield teams. This is a strong positive signal. Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning. Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices. Experience mentoring ...

time network telemetry and automation triggers. Define observability strategies covering metrics, logs, traces, and network telemetry pipelines. Architect AIOps capabilities including closed‐loop automation, anomaly detection, and predictive analytics for network operations. Integrate OSS observability with cloud‐native monitoring stacks (Prometheus, Grafana, OpenTelemetry, Elasticsearch). Lead design … automation design for network operations. Experience with observability tooling: OpenTelemetry, Prometheus, Grafana, Jaeger, Loki, ELK Stack. Knowledge of ML/AI model integration for anomaly detection, root‐cause analysis, and predictive network management. Strong grasp of TM Forum frameworks: eTOM, TAM, SID, and Open Digital Architecture ...

building practical tools to identify and resolve issues before they reach customers. The Role You will: Design and develop data quality checks covering validation, anomaly detection, freshness and coverage. Own and improve production web-scraping infrastructure, including Playwright, proxies and anti-bot handling. Build and maintain data … ship solutions efficiently. About You Strong Python and SQL skills, with hands-on dbt and BigQuery experience. Proven experience designing data quality, validation and anomaly detection processes. Production experience with web scraping at scale, ideally using Playwright or an equivalent. Experience with big data sets ...

versus standing access, and risk-gated credential issuance (e.g. a short-lived token issued against a CrowdStrike-style risk score). Behavioural baselining/anomaly detection for workloads — defining "normal" for a recurring workflow and catching deviation at volume. SIEM integration and action attribution: distinguishing an agent … onboarding standard (e.g. mandatory MCP interfaces) and win adoption through better defaults and developer experience, not mandate alone. Design SIEM integration, behavioural baselining, and anomaly detection for agentic workflows, and centralise siloed audit logs to satisfy both security and regulatory requirements. Take bounded beachheads (for example, authenticate ...

portfolio: a unified, real-time operations platform that connects OT and IT data across assets, processes and systems, applies AI-powered insights and anomaly detection, and helps organisations turn operational signals into measurable business outcomes. The role sits at the centre of Product, Sales and Presales. Acting … recognised internal subject matter expert. Areas of focus include: Operational Intelligence and its role in improving operational performance, decision-making and reliability. Industrial AI, anomaly detection, predictive insights and operational optimisation. Asset-centric and industrial software categories, including EAM, APM, Industrial Analytics and IIoT. Ownership: Treats commercial ...

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 ...

translating findings into practical actions. Using your expertise, you'll design and deploy AI-driven security capabilities such as threat detection, anomaly detection and incident response. You'll also advise on risk, compliance and regulation, ensuring we continue to meet industry standards as AI technologies ...

scalability, and ease of maintenance. AI/ML Model Development Develop, train, and evaluate machine learning models that address core FinOps use cases: cost anomaly detection, spend forecasting, workload rightsizing recommendations, and commitment coverage optimization. Implement and maintain MLOps pipelines for model versioning, automated retraining, performance monitoring … drift detection as cloud usage patterns evolve. Experiment with and apply generative AI and LLM capabilities to FinOps workflows - such as natural language interfaces for cost querying, AI-assisted tagging remediation, or intelligent cost allocation suggestions. Collaborate with FinOps analysts to validate model outputs against business expectations, refining ...

What You'll DoData Engineering & Data Products & EnablementOwn the full lifecycle of business critical data pipelines: define SLAs, implement performance measurement, monitoring, alerting, and anomaly detection, and lead incident response and root-cause analysis. Define scalable, reusable data models and curated data sets for analytics, data science ...

than AI tooling alone. What you'll be doing: Machine Learning & Advanced Analytics · Develop machine learning models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection. · Apply statistical and machine learning techniques to analyse structured and semi-structured datasets. · Perform exploratory data analysis, feature engineering, model training, validation ...

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. ...

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. ...

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 As defined by FinOps.org, this role works with all key FinOps personas: Executives ...