13 of 13 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 ...

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 ...

functions You will have: Strong experience building probability of default or credit scoring models Advanced Python capability Experience with algorithms such as logistic regression, XGBoost, LightGBM or similar A strong understanding of model evaluation (ROC-AUC, Gini, precision/recall) Experience working with complex financial or behavioural datasets You understand ...

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 ...

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 ...

production-grade AI solutions in complex, regulated environments Accelerate outcomes using pre-built frameworks and accelerators Core Capabilities AI/ML Engineering: TensorFlow, PyTorch, XGBoost, transformer architectures MLOps & LLMOps: MLFlow, Kubeflow, SageMaker, KServe, vector DBs Cloud & Platform Engineering: AWS, Azure, GCP, Kubernetes, Terraform Data Platforms: Spark, Kafka, Snowflake, Airflow Responsible ...

/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 ...

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 ...