14 of 14 XGBoost Jobs in London

grade analytical solutions. Hands-on experience with cloud platforms (Azure, AWS and/or Snowflake). Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong knowledge of MLOps ...

understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community ...

Computer Science, Mathematics, or similar Experience delivering predictive analytics or machine learning solutions Strong skills in Python, SQL, and ML libraries (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda, Redshift) Ability to clearly communicate insights to non-technical stakeholders Strong analytical thinking, collaboration ...

libraries such as Pandas, NumPy, and scikit-learn. Experience with frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow ...

should be genuinely strong across most of them and honest about where you want to grow. Supervised & Ensemble Methods. Gradient-boosted trees (XGBoost, LightGBM, CatBoost) for tabular forecasting; understanding of when tree-based models outperform neural approaches on structured data, and vice versa. Strong intuition for regularisation, hyperparameter tuning ...

recommendation systems, optimisation, or marketing analytics. Deep understanding of ML fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment tracking, model registries, monitoring ...

engineering, AI engineering, or applied data science. Proven track record deploying AI/ML solutions into production. Expertise with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Solid experience with LLMs, prompt engineering, RAG, and evaluation. Strong software engineering and MLOps skills (CI/CD, containers, monitoring, model registries ...

from women who are passionate about machine learning and eager to grow: Strong Python skills and experience with ML libraries (pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch). Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe). Experience with ML lifecycle tools (MLflow ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing ...

task - specific metrics ) . Expertise in applied ML, with hands-on experience building models using Python and relevant libraries and frameworks (e.g., scikit-learn, XGBoost , PyTorch )."" Experience with vector databases , data querying languages and retrieval methods. A commercial mindset, you understand how data science drives value, not just insight ...

modern AWS production environments. The Technical Reality: We operate a pragmatic stack where Linear Regression remains vital for stability and baseline performance, while XGBoost and LLMs are used as responsible additions. We are looking for someone who knows when to use a simple linear model and when to deploy … Responsibilities Predictive Modelling (Linear & Non-Linear): You will build and maintain foundational Linear Regression models for credit, affordability, and fraud scoring, while developing advanced XGBoost models for deeper risk insights. You will mine data to find behavioural signals—such as spending volatility or income stability—that predict affordability, repayment ...

Python. • Background in credit risk, rating agency modelling, or customer behaviour modelling. • Confident communicator with senior stakeholders. • Leadership experience in technical teams. • Experience with XGBoost or other boosting techniques. • Knowledge of MLOps or R is a plus but not essential. What They Offer • £120,000 to £150,000 base salary. ...

models across the customer lifecycle. THE ROLE The team builds and maintains all credit scoring and decisioning models using techniques such as GBM and XGBoost, owning the end-to-end model lifecycle from development through to monitoring and optimisation. Lead and develop a high-performing modelling team Remain hands ...

Science , you will need: • Strong hands on experience building credit or risk models. • Strong experience developing models in python. • Confident with techniques such as XGBoost or GBM. • Ability to link modelling decisions to commercial outcomes. • Clear communication and strong stakeholder management skills being able to manage both upwards and downwards. ...