26 to 50 of 53 XGBoost Jobs

Python and SQL; comfortable working with data structures, functions, and tools like Jupyter or VSCode • Exposure to time series forecasting techniques (e.g., ARIMA, Prophet, XGBoost, LSTM) and understanding of trend and seasonality patterns • Familiar with model evaluation metrics such as MAE, RMSE, MAPE, and R • Experience with version control tools ...

survival analysis). Solid object-oriented programming skills in Python and hands-on expertise with the core data science stack (Pandas, Numpy, Scikit-learn, XGBoost/LightGBM). A proven ability to break down vague, complex problems into concrete, solvable steps. Excellent communication and data storytelling skills. You can build ...

Data Science & AI Graduate Scheme (Edinburgh) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper, and progress ...

Data Science & AI Graduate Scheme ( Leeds and Halifax ) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper ...

Data Science and AI Industrial Placement (Bristol) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper, and ...

Data Science & AI Graduate Scheme (Bristol) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper, and progress ...

Data Science and AI Industrial Placement (Edinburgh) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper, and ...

Data Science and AI Industrial Placement (Leeds) Your AI era starts now At Lloyds Banking Group, data isnt just something we store in the cloud. Its the juice that keeps ideas flowing, decisions sharper, and ...

Learning Engineer, or a PhD in Computer Science with ML coursework Strong Python skills with experience using libraries such as TensorFlow, scikit-learn, and XGBoost Solid experience in data preparation, model training, and evaluation How to Apply: To apply for this job speak to Jack, our AI recruiter. Step 1. ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...

with data preprocessing, feature engineering, and evaluation at scale Familiarity with LLMs or modern foundation models Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers) Comfortable working across APIs, data stores, and infrastructure, not just notebooks Bias to ship, measure, and refine rather than chase perfect ...