26 to 50 of 51 XGBoost Jobs in England

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 ...

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 ...

market microstructure. Hands-on with interactive visualization libraries (e.g., Bokeh). Distributed compute experience (Dask, Ray). Familiarity with ML frameworks (JAX, PyTorch, TensorFlow, XGBoost). Experience with compilers and code generation. Benefits International environment (English as the main language). Pension scheme. Comprehensive health coverage. Team events and offsites. ...

ExData visualisation tools (PowerBI) and manipulating data (Python/Excel/VBA/SQL/Databricks/Spark), Analytical models (Python: SciKitLearn, Imblearn, Prophet, XGBoost, ExtraTrees, Kmeans Clustering), Web Scraping (Python: Selenium, Chrome driver, BeautifulSoup) and understanding of Databases (Oracle/SAP Hana/AWS). At Evri, we know ...

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 ...

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 ...