3 of 3 Remote/Hybrid Anomaly Detection Jobs in West Yorkshire

toxic combinations, identity exposure, control gaps, and vulnerability prioritization. Build and evaluate graph algorithms and ML models including centrality, community detection, similarity, anomaly detection, node classification, link prediction, embeddings, and Graph Neural Networks. Design GraphRAG and knowledge-grounded security assistants that combine graph traversal, vector … JanusGraph . Graph data science: Neo4j Graph Data Science, NetworkX , PyTorch Geometric or DGL; graph embeddings, pathfinding, similarity, clustering, link prediction, node classification, anomaly detection, and GNN development. Programming and engineering: Advanced Python and SQL; APIs; test automation; data structures; distributed processing; Git; CI/CD; containers ...

should expect to join customers periodically and for technical discussions. Engineers who never meet the customer build the wrong thing. KEY RESPONSIBILITIES Take our anomaly detection work from proof of concept into production across the estate, moving from retrospective analysis to live early warning. Build the validation … stack. Hands‐on experience with time‐series, sensor or telemetry data at scale, including the realities of gaps, drift, flatlines and misbehaving devices. Applied anomaly detection or predictive modelling taken into production, with the judgement to keep methods robust and explainable rather than elaborate. Practical experience building ...

libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...