11 of 11 Anomaly Detection Jobs in Central London

to live calls and chats, reads our customers’ knowledge bases, and drafts high‐quality replies for human agents - while also turning messy, multi‐channel feedback into trustworthy structured insights , anomaly detection , and novelty discovery . As the Tech Lead, you’ll own the technical vision and turn requirements into a live, reliable product used by brands like … and draft responses with human‐in‐the‐loop controls. Structured extraction: Schema‐driven pipelines over unstructured text (and other modalities) using retrieval, tool‐use, and robust LLM prompting. Hybrid anomaly detection: Blend classical time‐series methods (e.g., decomposition, change‐point, forecasting) with LLM‐aware, contextful detectors for seasonality, spikes, step‐changes, and drift. Novelty discovery: Embedding‐based … crisp docs. Bonus points DevOps wizardry; GPU/accelerator experience. Multimodal pipelines (text + voice + screenshots). Prior experience in contact center/CX analytics or novelty/anomaly systems. Founder or founding engineer experience More ❯

to live calls and chats, reads our customers’ knowledge bases, and drafts high‐quality replies for human agents - while also turning messy, multi‐channel feedback into trustworthy structured insights , anomaly detection , and novelty discovery . As the Tech Lead, you’ll own the technical vision and turn requirements into a live, reliable product used by brands like … and draft responses with human‐in‐the‐loop controls. Structured extraction: Schema‐driven pipelines over unstructured text (and other modalities) using retrieval, tool‐use, and robust LLM prompting. Hybrid anomaly detection: Blend classical time‐series methods (e.g., decomposition, change‐point, forecasting) with LLM‐aware, contextful detectors for seasonality, spikes, step‐changes, and drift. Novelty discovery: Embedding‐based … crisp docs. Bonus points DevOps wizardry; GPU/accelerator experience. Multimodal pipelines (text + voice + screenshots). Prior experience in contact center/CX analytics or novelty/anomaly systems. Founder or founding engineer experience More ❯

within the SOC, responsible for handling escalations from Tier 1 and Tier 2 analysts and managing the most complex security incidents. You will lead deep-dive investigations, improve detection and response processes, and play a key role in stakeholder engagement. Key responsibilities include: Analysing advanced security incidents, determining root cause and attack paths. Leading incident response activities across … will bring: Significant experience in SOC operations and cyber incident response. Strong knowledge of adversary TTPs and frameworks such as MITRE ATT&CK. Hands-on skills in log analysis, anomaly detection, and incident remediation. Experience working in traditional IT and cloud (Azure and/or AWS) environments. Strong communication skills with the ability to translate technical threats More ❯

Hosting, Google IAM, Secret Manager Azure: Azure OpenAI, Azure App Services, Azure AD authentication Cybersecurity: CSP headers, XSS/CSRF protection, OAuth2 flows, JWT encryption Front-end logging for anomaly detection and audit trails Familiarity with OWASP guidelines and secure SDLC practices DevOps: GitHub Actions, Docker, Terraform, security scans in CI/CD (e.g., Snyk, Dependabot or More ❯

and AI-native querying (e.g., Text-to-SQL). Productize the PLuG SDK as a Data Platform: Leverage in-app instrumentation (session replays, logs, engagement events) to power observability, anomaly detection, and customer experience analytics. Build AI-Enabled Interactions: Drive the development of agentic features including natural language queries, AI-generated dashboards, and real-time recommendations—bridging More ❯

and AI-native querying (e.g., Text-to-SQL). Productize the PLuG SDK as a Data Platform: Leverage in-app instrumentation (session replays, logs, engagement events) to power observability, anomaly detection, and customer experience analytics. Build AI-Enabled Interactions: Drive the development of agentic features including natural language queries, AI-generated dashboards, and real-time recommendations—bridging More ❯

models that power live decision-making. What You'll Be Doing: Designing and deploying production-grade ML models that drive live decision-making. Applying computer vision Building fraud and anomaly detection models using geospatial and behavioural signals. Partnering with engineers to scale pipelines, compliance monitors, and detection systems. Leading scientific direction and mentoring other data More ❯

key business services. Utilize Splunk ES for security event monitoring and correlation searches. Automate tasks and integrate systems using Python, Shell, or Perl scripting. Perform root cause analysis and anomaly detection by analyzing complex log data. Requirements: 10+ years experience in observability, with deep expertise in Splunk, especially ITSI. Proficiency in Scripting (Shell/PowerShell/Python More ❯

suppliers to review observability standards. What You'll Need: * The ability to demonstrate your extensive experience in designing and configuring the following within Dynatrace: o Application performance monitoring o Anomaly detection profiles o Alerting rules and alert profiles o Synthetic monitoring o Log monitoring o Real User Monitoring (RUM) to capture and analyse end-user experience across More ❯

technical consultant to ensure observability efficiency and AIdriven insights across the business. Key skills/experience required: Experience designing and configuring advanced monitoring solutions using Dynatrace tools, including APM, anomaly detection, alerting, synthetic and log monitoring, RUM, DQL, and system integrations. Help maintain automatic end-to-end business flow visualizations in Dynatrace for key business processes. Proved More ❯

business needs into data-driven solutions Essential skills: Python (pandas, NumPy, scikit-learn) – data wrangling, modelling, feature engineering SQL – querying structured datasets Model Development & Validation – classification, unsupervised learning (outlier detection), ranking models ML Deployment – containerised deployments (Podman, SageMaker, DSW pipelines) Git – version control for reproducible workflows Time-Series Analysis – spotting risk trends over time EDA – identifying early risk … signals and clusters Desirable skills: Rank aggregation/ensemble methods (e.g. Robust Rank Fusion) Model explainability tools (SHAP, LIME) Model monitoring & drift detection Experience in RegTech, FinCrime, or data-led supervision projects Please submit your CV, highlighting relevant experience. More ❯