11 of 11 Permanent XGBoost Jobs in the UK excluding London

modelling and applied AI , developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large Language Models and autonomous agent systems ...

modelling and applied AI , developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large Language Models and autonomous agent systems ...

modelling and applied AI, developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large Language Models and autonomous agent systems ...

modelling and applied AI, developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large Language Models and autonomous agent systems ...

deploying, and maintaining production ML solutions Broad experience across multiple ML domains, including: Forecasting/Time-Series Modelling Regression Classification Gradient Boosting models (e.g. XGBoost, LightGBM) Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch) Experience with model evaluation, performance monitoring, and accuracy metrics Version control (Git) Experience working with cloud ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g, GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

instinct for leakage, bias and overfitting in messy real-world data. Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool. A deep understanding of how modern models are built ...

with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience developing and implementing ...

Model Development, Governance & Explainability Deep experience applying a wide range of machine learning techniques to real‐world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation. Strong knowledge of model ...

Raffles, Singapore | Posted on 04/06/2026 Experience working with ML platforms such as CML, SparkMLlib, and Python ML libraries (scikit‐learn, XGBoost), including modeldeployment. Design and develop highly scalable, Real time systemsusing Hadoop ecosystem components(Iceberg, Spark, Ozone, Trino, Hive,Ranger, Kafka, Flink and Nifi) Build robust ...

underpin your work and help them productionise your applications Preferred Technical Skills Time-series modelling (ARIMA, SARIMA, etc.) and - Tree-based & gradient boosting models (XGBoost, LightGBM, NGBoost) Expertise/knowledge of internally stored data & data consumers as well as other data sources that are currently not databased Python (3+ … SQLAlchemy, Streamlit Infrastructure and DB: Postgres, Warehousing (if we did it), prefect, Kubernetes, SQLAlchemy, AWS Visualisation: Grafana, Superset, Marimo Forecasting + general DS: lightgbm, xgboost, numpy, scipy, scikit-learn Claude AI Ultimately we are looking for someone who is a great fit for our company so we encourage ...