12 of 12 Remote XGBoost Jobs in England

What we offer Solid understanding of computer science fundamentals, data structures, algorithms, data modelling and software architecture Solid understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art areas (e.g. NLP, Transfer Learning) and modern Deep Learning algorithms (e.g. BERT, LSTM) Solid knowledge of SQL and Python's ecosystem for data analysis (Jupyter More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

key machine learning models, including Gradient Boosting Machines (GBMs), Neural Networks and Large language models (LLMs). Hands-on experience with popular machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Knowledge of AWS products and services including Sagemaker. Deep knowledge of Microsoft Excel in a commercial setting. You enjoy being Agile - you should be happy working More ❯

solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI/CD More ❯

latent class, hierarchical, and factor analysis. Understanding of working with market research and customer databases. Knowledge of statistics and machine learning applications. Experience with predictive modeling using linear models, XGBoost, discriminant analysis, etc. Beneficial Skills Experience with conjoint analysis and other pricing methods. Knowledge of GDPR and data compliance. Experience working with third-party data vendors and APIs. Some familiarity More ❯

services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning Engineer, timeseries, forecasting, VertexAI More ❯

services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning Engineer, timeseries, forecasting, VertexAI More ❯

skills for AI/ML applications - clean, well-tested code, version control best practice, and smooth integration with back-end systems. Hands-on experience with gradient-boosted models (e.g., XGBoost, LightGBM) and/or building LLM-powered systems (e.g. tool/function calling, prompt design, evals) for text-heavy tasks. Solid data skills: SQL, pandas, experiment tracking, and cloud deployment More ❯