14 of 14 XGBoost Jobs in the UK

natural language processing, computer vision, and recommender systems Strong programming skills in Python, SQL and proficiency with ML libraries (scikit‐learn, pandas, NumPy, XGBoost, LightGBM) Strong analytical and problem‐solving mindset with attention to detail Ability to work independently and drive projects from ambiguous requirements Story telling with data ...

natural language processing, computer vision, and recommender systems Strong programming skills in Python, SQL and proficiency with ML libraries (scikit‐learn, pandas, NumPy, XGBoost, LightGBM) Strong analytical and problem‐solving mindset with attention to detail Ability to work independently and drive projects from ambiguous requirements Storytelling with data and insights ...

solutions in industry or engineering environments. Strong expertise in Python and AI/ML libraries: PyTorch, TensorFlow, Scikit‐Learn, Keras, NumPy, Pandas, JAX, XGBoost, PyTorch Geometric, OpenCV, Transformers, LangChain, Pinecone . Proven hands‐on experience deploying models in production and building scalable AI software. Deep understanding of advanced modelling methods ...

Science, Statistics, Mathematics, or related quantitative fields preferred. Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikit‐learn, XGBoost, TensorFlow or PyTorch. Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets. Deep understanding ...

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 ...

/CD, Docker, Kubernetes, APIs What you bring Core requirements 5+ years in data science/machine learning Strong foundation in traditional ML (e.g. XGBoost, regression, statistical modelling) Hands-on experience with LLMs/GenAI/NLP/AI systems Experience deploying models into production (not just experimentation) Familiarity with ...

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 ...

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 ...

/AI research and incorporate cutting-edge techniques and frameworks when applicable. Utilize ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, XGBoost, and others for model building and experimentation. Ensure machine learning solutions are scalable and optimized for performance on large datasets or real-time systems. Maintain clear ...

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 ...

building and deploying ML models at scale. Proven ability to lead cross‐functional technical initiatives and influence engineering strategy. Proficiency inPython(with libraries likePyTorch, XGBoost, Scikit‐learn) andSQL. Strong experience with machine learning pipelines and orchestration tools such asAirflow,SageMaker Pipelines, or similar. Deep understanding of machine learning fundamentals, including ...

identify gaps between predicted vs. actual claim costs Working with large, messy insurance datasets across multiple product lines Tools & tech: Python, SQL, AzureML, XGBoost Opportunity to mentor within the team ...