3 of 3 Anomaly Detection Jobs in Leeds

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 … ArangoDB, or 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/ ...

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 ...

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 ...