15 of 15 Permanent XGBoost Jobs in London

grade analytical solutions. Hands-on experience with cloud platforms (Azure, AWS and/or Snowflake). Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong knowledge of MLOps ...

will have the following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits ...

Computer Science, Mathematics, or similar Experience delivering predictive analytics or machine learning solutions Strong skills in Python, SQL, and ML libraries (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda, Redshift) Ability to clearly communicate insights to non-technical stakeholders Strong analytical thinking, collaboration ...

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 ...

recommendation systems, optimisation, or marketing analytics. Deep understanding of ML fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment tracking, model registries, monitoring ...

should be genuinely strong across most of them and honest about where you want to grow. Supervised & Ensemble Methods. Gradient-boosted trees (XGBoost, LightGBM, CatBoost) for tabular forecasting; understanding of when tree-based models outperform neural approaches on structured data, and vice versa. Strong intuition for regularisation, hyperparameter tuning ...

engineering, AI engineering, or applied data science. Proven track record deploying AI/ML solutions into production. Expertise with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Solid experience with LLMs, prompt engineering, RAG, and evaluation. Strong software engineering and MLOps skills (CI/CD, containers, monitoring, model registries ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing ...

forecasting and time-series analysis Hands-on with Python & SQL (Pandas, NumPy, Scikit-learn, Statsmodels) Experience with Prophet, ARIMA and/or Gradient Boosting (XGBoost) Comfortable working in fast-changing environments with imperfect data Clear communicator — able to influence both technical and commercial stakeholders 💥 Why this role stands out Real ...

professional experience) Demonstrated commercial impact through applied statistics and predictive modelling Experience with forecasting and probabilistic approaches, including ML techniques (e.g. SARIMA, Monte Carlo, XGBoost, ANN) High proficiency in Python and SQL Comfortable operating autonomously, making judgement calls, and navigating ambiguity Nice to have Experience within a consulting or advisory ...

modern AWS production environments. The Technical Reality: We operate a pragmatic stack where Linear Regression remains vital for stability and baseline performance, while XGBoost and LLMs are used as responsible additions. We are looking for someone who knows when to use a simple linear model and when to deploy … Responsibilities Predictive Modelling (Linear & Non-Linear): You will build and maintain foundational Linear Regression models for credit, affordability, and fraud scoring, while developing advanced XGBoost models for deeper risk insights. You will mine data to find behavioural signals—such as spending volatility or income stability—that predict affordability, repayment ...

Python. • Background in credit risk, rating agency modelling, or customer behaviour modelling. • Confident communicator with senior stakeholders. • Leadership experience in technical teams. • Experience with XGBoost or other boosting techniques. • Knowledge of MLOps or R is a plus but not essential. What They Offer • £120,000 to £150,000 base salary. ...

group with strong influence across risk, product, and strategy. The Role Lead credit decisioning strategy and model development. Stay hands on with modelling using XGBoost, GBM and related techniques. Oversee end-to-end model lifecycle from design to monitoring. Partner with risk, product, and engineering on decisioning and portfolio performance. ...

models across the customer lifecycle. THE ROLE The team builds and maintains all credit scoring and decisioning models using techniques such as GBM and XGBoost, owning the end-to-end model lifecycle from development through to monitoring and optimisation. Lead and develop a high-performing modelling team Remain hands ...

Science , you will need: • Strong hands on experience building credit or risk models. • Strong experience developing models in python. • Confident with techniques such as XGBoost or GBM. • Ability to link modelling decisions to commercial outcomes. • Clear communication and strong stakeholder management skills being able to manage both upwards and downwards. ...