11 of 11 XGBoost Jobs in London

equivalent. Exposure to observability/monitoring systems (Prometheus, Grafana, ELK, Datadog) is a plus. Proficiency in Python and familiarity with ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch). Strong experience with data manipulation and pipelines using Pandas, NumPy, and SQL. Knowledge of containerized deployments (Docker, Kubernetes) and cloud ML services ...

efficiency, leveraging end-to-end ML architectures spanning supervised, unsupervised, and reinforcement learning. Combine statistical methods with modern ML techniques such as gradient boosting (XGBoost, LightGBM), ensemble methods, deep learning (CNN, RNN, LSTM), and generative models (GAN, VAE) to enhance predictive accuracy, interpretability, and automation. Engineer scalable analytical frameworks ...

Core: Data Manipulation (SQL, Pandas, Pyspark) Azure AI (Azure AI Foundry, AI Search, Document Intelligence, AI Services) Data Science & Machine Learning (Databricks, Python, SKLearn, XGBoost, MLFlow, EDA) Familiarity with LLMs (OpenAI, Prompt Engineering, LangChain) Relevant Azure Data & Computation services (ADLS, ADF, Databricks, SQL Databases) Supporting: Azure ML Services React/ ...

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 ...

lifecycle, from experimentation through deployment and monitoring. Proficiency in Python and familiarity with ML libraries like scikit-learn, XGBoost, PyTorch or TensorFlow. Experience with ML Ops tools and practices such as MLflow, model registries, containerisation (Docker/Kubernetes), and CI/CD pipelines. Comfortable working with cloud platforms (preferably Azure ...

Data Scientist to develop long term roadmaps and be comfortable presenting to Data leaders when needed. Strong working knowledge of modern ML frameworks (e.g., XGBoost, Scikit-learn, TensorFlow) and experience applying them to real-world problems. Proficiency in writing clean, maintainable, and efficient code in Python and SQL. Solid understanding ...

training and deployment, model serving, and effective data lifecycle management. Expert proficiency in Python, SQL, and core ML libraries (e.g., Pandas, NumPy, Scikit-Learn, XGBoost). Strong experience working with cloud-based ML platforms (AWS preferred, e.g., SageMaker). Familiarity with model management and deployment tooling such as Git, MLflow ...

applications No domain expertise required — curiosity and adaptability are key Technical skillset: Must-have: Python, data science/ML libraries (pandas, NumPy, scikit-learn, XGBoost, PyTorch, Hugging Face), experiment design, evaluation metrics Nice-to-have: LLM fine-tuning, prompt engineering, SQL/PostgreSQL How We Work We live ...

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 ...

look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning libraries like PyTorch, XGBoost and JAX Understanding of crypto or traditional financial markets Strong API design and documentation skills What do you get in return? Up to 250k base (depending ...

look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning libraries like PyTorch, XGBoost and JAX Understanding of crypto or traditional financial markets Strong API design and documentation skills What do you get in return? Up to £250k base (depending ...