11 of 11 XGBoost Jobs in London

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g, GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). \n \n\n \n \nExperience with data storage for AI, vector databases, semantic search, and knowledge graphs. \n \n\n \n \nContributions to open ...

modern deep learning frameworks (e.G. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.G, GPT, Llama, Claude, Gemini), machine learning libraries (e.G. scikit-learn, XGBoost).\ N\n\n\n\ NExperience with data storagefor AI, vector databases, semantic search, and knowledge graphs.\ N\n\n\n\ NContributions to open-sourceAI ...

deep learning frameworks (e.G. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.G., GPT, Llama, Claude, Gemini), and advanced ML libraries (e.G. scikit-learn, XGBoost).\ N\n\n\n\ NExperience with data storagefor AI, vector databases, semantic search, and knowledge graphs.\ N\n\n\n\ NActive contribution to open ...

model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud data ...

engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \ N\n\nPractical knowledge of ML frameworks (e.G. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \ N\nPlatform & Cloud Engineering: \ N\nArchitecture and delivery of scalable cloud data ...

developing and deploying credit risk models, including: Probability of Default (PD) models Scorecards Risk segmentation models Experience evaluating and implementing challenger models such as XGBoost while balancing explainability and regulatory requirements. Strong understanding of model performance metrics including AUC, Gini, KS, calibration, and business impact measurement. Experience deploying production-ready ...

customer-facing or consulting roles is strongly preferred Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment Engineering best practices: You write well-structured ...

have a general understanding of REST APIs (design, integration, authentication). You have experience building, tuning, or deploying ML models (e.g., scikit‐learn, XGBoost, PyTorch, TensorFlow) in production settings. Experience with AI use cases in financial services (e.g. fraud detection, credit scoring, underwriting) is a strong plus. You are comfortable ...

Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services. Expert‐level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost). Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management. Advanced experience designing explainability systems, reason codes, and governance ...

problems and solutions to technically guide more junior data scientists.· Expert in applied machine learning, including the techniques currently being used in PRISM (XGBoost and SHAP)· Experience of using machine learning to solve commercial predictive· Ideally, experience with Causal MLBoth roles require Stakeholder management skills (defining and prioritising requirements, contributing ...

problems and solutions to technically guide more junior data scientists.· Expert in applied machine learning, including the techniques currently being used in PRISM (XGBoost and SHAP)· Experience of using machine learning to solve commercial predictive· Ideally, experience with Causal MLBoth roles require Stakeholder management skills (defining and prioritising requirements, contributing ...