13 of 13 Anomaly Detection Jobs in London

of rough path theory and modern machine learning. This ambitious, multi-institutional collaboration aims to redefine how streamed data is modelled and processed-unlocking new capabilities in generative AI, anomaly detection, and real-time decision-making. What you would be doing Key scientific challenges you'll help tackle: Next-generation Transformers: Develop mathematically grounded architectures for continuous … multimodal data streams. Efficient Representations: Create robust, interpretable, and scalable representations using signature and path development techniques Anomaly Detection: Build principled, representation-invariant methods for identifying outliers in high-dimensional data with use in appllications. The postholders will be expected to make significant contributions to the mathematical foundations of the programme, engage actively with the broader DataSıg More ❯

and billions of transactions annually. Leveraging proven data science, including our proprietary consortium data, our customers use our risk-based, machine learning platform to deliver the highest fraud detection rates, lowest false positive rates, and lowest customer intervention in the industry. We stop fraud, not customers. See what others can't at What you'll achieve As a … analysis to uncover trends and actionable insights. Create data visualizations, reports, dashboards, and perform data audits. Leverage predictive models to optimize customer experiences and drive business outcomes. Create automated anomaly detection systems to monitor and ensure data quality and operational performance. Qualifications Bachelor's degree in Data Science, Computer Science, or a related field. Doctorate in a … of data engineering, analysis and analytics, as well as machine learning. 6+ years of experience working in Data Science, preferably within a Software organization. Experience with financial fraud detection and prevention is a major advantage. Advanced proficiency in Python for data science and machine learning applications. Familiarity with cloud-based infrastructure, preferably Azure. Expertise with Data Warehousing/ More ❯

access controls, and auditing). Collaborate with finance and data teams to define data models, schemas, and governance policies that support modern finance workflows, including automated reporting, forecasting, and anomaly detection. Implement data retrieval mechanisms optimized for LLM-based querying via Co-pilot and/or similar tools, enabling natural language access to financial data while maintaining accuracy and More ❯

contextualisation, and decision-making using advanced AI models. Intelligent Interfaces: Develop adaptive, conversational UI/UX elements powered by LLMs and logic-driven agents. Analytics & Insights: Build predictive analytics, anomaly detection, and KPI dashboards that empower clients to act with confidence. AI Orchestration (MCP): Oversee integration of Model Context Protocol across product modules for scalable, context-aware More ❯

contextualisation, and decision-making using advanced AI models. Intelligent Interfaces: Develop adaptive, conversational UI/UX elements powered by LLMs and logic-driven agents. Analytics & Insights: Build predictive analytics, anomaly detection, and KPI dashboards that empower clients to act with confidence. AI Orchestration (MCP): Oversee integration of Model Context Protocol across product modules for scalable, context-aware More ❯

contextualisation, and decision-making using advanced AI models. Intelligent Interfaces: Develop adaptive, conversational UI/UX elements powered by LLMs and logic-driven agents. Analytics & Insights: Build predictive analytics, anomaly detection, and KPI dashboards that empower clients to act with confidence. AI Orchestration (MCP): Oversee integration of Model Context Protocol across product modules for scalable, context-aware More ❯

contextualisation, and decision-making using advanced AI models. Intelligent Interfaces: Develop adaptive, conversational UI/UX elements powered by LLMs and logic-driven agents. Analytics & Insights: Build predictive analytics, anomaly detection, and KPI dashboards that empower clients to act with confidence. AI Orchestration (MCP): Oversee integration of Model Context Protocol across product modules for scalable, context-aware More ❯

and investment planning. Knowledge of KPIs and ability to architect solutions that improve financial and operational performance. Exposure to AI/ML use cases in corporate functions (e.g. finance anomaly detection, HR attrition modelling, procurement spend analytics) Job Offer Competitive salary of £100,000 to £130,000 per annum. Bonus target of 18% to reward performance. Private More ❯

in applying statistical models for large-scale application and building automated analytical systems Preferred Qualifications Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection) Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems Bachelor's degree in Computer Science or More ❯

principles and statistical modeling. Day-to-day responsibilities include: Collaborate with other Data Scientists and Machine Learning Engineers to design and implement analytical approaches that inform scalable threat detection models. Partner with threat intelligence analysts to understand emerging attack techniques and leverage data to develop novel detection strategies and expose findings. Perform in-depth data analysis … to identify trends, relationships, and anomalies within vast security datasets. Develop, validate, and refine statistical and machine learning models for threat detection and intelligence generation. Monitor the performance of analytical models and threat intelligence pipelines, continuously identifying opportunities for improvement and refinement. Investigate and resolve data-related issues in production environments, ensuring the accuracy and integrity of our … to support your learning on the job, but prior experience is a plus: Experience with large-scale data processing frameworks (e.g., Spark, Flink). Experience with time series analysis, anomaly detection, or graph analytics in a security context. Proficiency in data visualization tools and techniques to effectively communicate complex findings. A basic understanding of the cyber threat More ❯

and efficiency. Leverage AWS technologies (S3, Athena, QuickSight) to analyse data from millions of field devices, delivering insights to inform decision-making and drive operational efficiency. Develop and implement anomaly detection techniques and data-driven solutions to proactively identify and resolve system issues. Perform global metric comparisons across various device models. Lead teams, mentor colleagues, and communicate More ❯

Skills & Expectations: Strong expertise in AI safety tools (e.g., Responsible AI Toolbox, Deepchecks) Experience with LLM compliance testing frameworks Hands-on knowledge in: Prompt injection testing and AI behaviour anomaly detection Bias, fairness, and explain ability metrics for LLMs Audit trail generation, logging, and compliance reporting (GDPR/DSAR) Security risk mitigation in AI agent systems More ❯

improvement using front end monitoring tools and backend KPI data Turn KPI trends into a cohesive story and deliver high level summaries to executive management Develop automated alerting and anomaly detection systems Assist with A/B experimentation monitoring and reporting Produce and maintain KPI queries, dashboards, and reports What You'll Bring Expertise in complex data More ❯