5 of 5 XGBoost Jobs in London

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 ...

with enterprise‐level infrastructure and architecture, including a extensive understanding of APIs. 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. ...

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 ...

deal scoring (classification, regression, ranking) Analyse financial, credit, and time-series data to extract predictive signals Build and benchmark models such as: Gradient boosting (XGBoost, LightGBM, CatBoost) Random Forests Logistic/linear regression Design LLM-powered workflows for: Document understanding (loan docs, credit memos, financials) Risk factor extraction Deal-level … Regression, classification, and ranking models Time-series analysis & feature engineering Hands-on experience with: Python (pandas, NumPy, scikit-learn, etc.) Gradient boosting frameworks (XGBoost/LightGBM/CatBoost) Experience working with LLMs (prompting, RAG, embeddings, evaluation) Ability to work across structured + unstructured data Strong communication skills – able to engage ...