10 of 10 Anomaly Detection Jobs in the City of London

monitoring and reporting. Technology & Automation · Use Python, SQL, and Databricks to analyse in house data. · Explore opportunities to apply AL and ML models to improve forecasting, scenario tests, and anomaly detection. Requirements · Proven experience in Python, SQL, and Databricks · Experience in building dashboards (Databricks, PowerBI, Tableau, or similar) · Experience in the financial industry · Strong analytical and problem-solving skills More ❯

monitoring and reporting. Technology & Automation · Use Python, SQL, and Databricks to analyse in house data. · Explore opportunities to apply AL and ML models to improve forecasting, scenario tests, and anomaly detection. Requirements · Proven experience in Python, SQL, and Databricks · Experience in building dashboards (Databricks, PowerBI, Tableau, or similar) · Experience in the financial industry · Strong analytical and problem-solving skills 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 ❯

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 ❯