1 to 25 of 57 Permanent XGBoost Jobs in the UK

command of English – both written and spoken (international client communication, presentations), Expertise in at least one of the following domains: Experience with TensorFlow, PyTorch, XGBoost, LightGBM, Proficiency with Git, Linux, Docker, MLflow, Airflow, AWS/GCP/Azure, Solid SQL and data visualization skills. Additional Information Nice to have: Proven ...

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 ...

statistical models within a commercial environment. Advanced proficiency in Python (pandas, NumPy, scikit‐learn) and advanced SQL. Solid understanding of core ML algorithms (e.g. XGBoost, LightGBM, linear/logistic regression, clustering, time‐series forecasting). Practical familiarity with software engineering best practice, including Git, code reviews and reproducible workflows. Awareness ...

learning or statistical models within a commercial environment.Advanced proficiency in Python (pandas, NumPy, scikit-learn) and advanced SQL.Solid understanding of core ML algorithms (e.g. XGBoost, LightGBM, linear/logistic regression, clustering, time-series forecasting).Practical familiarity with software engineering best practice, including Git, code reviews and reproducible workflows.Awareness of modern ...

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 ...

Strong expertise in Python and SQL. Deep familiarity with the Python data stack (pandas, NumPy, scikit-learn) and standard ML frameworks (such as PyTorch, XGBoost, or LightGBM). Engineering Rigor: Proven ability to build for production. Experience working in cloud-based environments (AWS or GCP) and familiarity with CI/ ...

Anaplan Applications, translating raw customer data into automated, high-value business insights. Advanced Modeling & Timeseries Forecasting: Design, train, and implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold-start forecasting problems. Feature Engineering Pipelines: Build and optimize scalable data ...

Anaplan Applications, translating raw customer data into automated, high‐value business insights. Advanced Modeling & Timeseries Forecasting: Design, train, and implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold‐start forecasting problems. Feature Engineering Pipelines: Build and optimize scalable data ...

Engineering: Python proficiency, modular API design, creating reusable libraries, advanced SQL, and scalable data integration using ML/AI frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face).AI Architecture & Security: Proven ability to architect secure enterprise cloud environments, ensuring stringent adherence to identity and access management (IAM) protocols, data governance ...

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 ...

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 ...

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. Actively contributes to open-source AI projects, research publications and industry ...

strategic AI solutions. Software/Data Engineering: Python proficiency, API design, SQL, data integration, and ML/AI libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers). Agentic AI Systems: Hands-on experience with agentic AI setups including LLM orchestration, tool use/function calling, RAG, agents, prompt engineering ...

churn/retention propensity, Customer Lifetime Value, pricing optimisation, demand forecasting, lead scoring or recommendation systems. Strong Python for machine learning (scikit-learn, XGBoost/LightGBM, pandas and statistical libraries), with clean, tested and version-controlled code. Advanced SQL and experience with large-scale cloud data platforms such as Databricks ...

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 ...

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 ...

deployment. Strong understanding of traditional machine learning alongside generative AI, with practical experience in supervised and unsupervised learning and algorithms/frameworks such as XGBoost, scikit-learn or equivalent. Experience implementing RAG architectures using embeddings, vector databases/search technologies and enterprise data sources, with an understanding of retrieval quality ...

deployment. Strong understanding of traditional machine learning alongside generative AI, with practical experience in supervised and unsupervised learning and algorithms/frameworks such as XGBoost, scikit-learn or equivalent. Experience implementing RAG architectures using embeddings, vector databases/search technologies and enterprise data sources, with an understanding of retrieval quality ...

skills and experience working with large-scale relational and cloud-based databases (e.g., Snowflake,BigQuery). Experience with machine learning frameworks (e.g., scikit-learn,XGBoost, TensorFlow,PyTorch). Ability to work autonomouslyand comfortable with ambiguity. Experience in the healthcare field, preferably in generic pharmaceuticals (pricing,sourcingor distribution) preferred, though notrequired. ...

Machine Learning systems from research all the way to deployment and maintenance Excellent Python programming skills and hands on experience with libraries such as XGBoost, PyTorch, TensorFlow, or Hugging Face Experience working with cloud‐based data and AI platforms, particularly Azure and Databricks Strong stakeholder engagement and storytelling capabilities; able ...

time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data science/statistics, signal research, backtesting or factor modelling; full stack ...

into a real product. B2B SaaS experience is a strong plus. Technical Core: Strong Python and applied ML libraries for tabular problems (scikit-learn, XGBoost or LightGBM, statsmodels or Prophet). Solid SQL expertise is required. ML & Modeling Depth: Depth in supervised learning, forecasting, ranking, recommendation, or optimisation. You have ...