11 of 11 Remote/Hybrid XGBoost Jobs in the UK

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 ...

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 ...

deploying models used in real world decision making (e.g. ensemble methods, regularised regression) Advanced proficiency in R, including tidyverse, data.table, caret or tidymodels, xgboost and Shiny Strong SQL skills with experience querying large scale relational databases such as Azure SQL or PostgreSQL Experience working with high volume or time based ...

client in conjunction with this vacancy only. KEY SKILLS AI, Python, Machine Learning, Software Engineering, Data, APIs, Cloud, Agile, Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, NLTK, OpenAI, LangChain, Pandas, NumPy, MLflow, Kubeflow, Docker, Kubernetes, Azure Machine Learning, Google Vertex ...

Machine Learning Engineer or equivalent role Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version ...

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 ...

commercial experience in machine learning or data science Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost Exposure to AWS services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation, and experimentation Familiarity with MLOps or experiment tracking tools (e.g. ...

Machine Learning Strong Understanding of what it actually takes to run ML Systems in Production Hands-On Experience with Deep Learning Frameworks (TensorFlow, PyTorch, XGBoost) Deep Knowledge of Data Warehousing, ETL/ELT Architectures & Heavy Analytic Environments (Apache Beam, Hadoop, Spark, Hive) Responsibilities: Leading Customer-Facing AI Deployments across Europe ...

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 ...

technical and non-technical stakeholders. Your Skills and Experience • Strong experience working with large, messy and diverse datasets. • Hands-on modelling experience using GLMs, XGBoost or similar predictive methods. • Proficiency in Python and SQL, ideally within a cloud environment such as Azure. • Experience combining or managing multiple models ...

technical and non-technical stakeholders. Your Skills and Experience * Strong experience working with large, messy and diverse datasets. * Hands-on modelling experience using GLMs, XGBoost or similar predictive methods. * Proficiency in Python and SQL, ideally within a cloud environment such as Azure. * Experience combining or managing multiple models ...