5 of 5 Remote/Hybrid XGBoost Jobs in the UK

resided in the UK for 5 years or over. Key Responsibilities Strong proficiency in Python and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch. Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe. Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ...

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 ...

members in identifying and addressing data science opportunities. Required Skills & Experience Strong proficiency in Python and ML libraries such as pandas, NumPy, scikit‐learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, and PyTorch. Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe. Familiarity with ML lifecycle tools: MLflow, Kubeflow ...

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 ...

Research, prototype, and deploy LLM use cases (Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you’ll bring Hands ...