5 of 5 Remote Anomaly Detection Jobs in London

Senior Machine Learning Engineer - Behavioural Modeling & Threat Detection - £160,000+ - Fully Remote UK BASED CANDIDATES ONLY My client is looking for an experienced Machine Learning Engineer ready to play a pivotal role in shaping the technical direction of their behavioural modelling and threat detection systems. This … especially in cross-functional contexts. Bonus Experience (Nice to Have) Exposure to large language models (LLMs) or foundational model adaptation. Previous work in cybersecurity, anomaly detection, or behavioural analytics. Familiarity with orchestration frameworks (Airflow or similar). Experience with scalable ML systems, pipelines, or real-time data ...

scalable and robust product architecture. Define and evolve chart-of-accounts mapping, ensuring alignment with industry-standard frameworks where applicable. Extend error- and outlier-detection logic, annualisation processes and export capabilities (Excel, API and additional formats as required). Partner with customers and internal stakeholders to understand pain ...

safety tools (e.g., Responsible AI Toolbox, Deepchecks) Familiarity with Azure AI Foundry and LLM compliance testing frameworks Prompt injection testing and AI behaviour anomaly detection This is a great role, and an awesome time to join this business as they expand their artificial intelligence team. For more ...

safety tools (e.g., Responsible AI Toolbox, Deepchecks) Familiarity with Azure AI Foundry and LLM compliance testing frameworks Prompt injection testing and AI behaviour anomaly detection This is a great role, and an awesome time to join this business as they expand their artificial intelligence team. For more ...

answers - avoid stochastic elements or require fixed random seeds for exact reproducibility Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations … comparison corrections Causal inference: propensity score matching, difference-in-differences Complex ETL pipeline logic and multi-source data integration Missing data imputation and outlier detection algorithms Feature engineering: encoding, interactions, dimensionality reduction Time Series & Forecasting Trend decomposition and seasonality detection Stationarity testing and autocorrelation calculations Forecast ...