26 to 50 of 57 XGBoost Jobs in the UK

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 ...

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 ...

cards, or BNPL. Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques. Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow. Experience with explainability frameworks such as SHAP, LIME, PDP. Ability to communicate technical concepts clearly and influence cross-functional decisions. Familiarity ...

opportunities to improve business efficiency through intelligent automation. Skills & Experience Strong Python programming skills with experience using libraries such as Pandas, NumPy, Scikit-learn, XGBoost or LightGBM, and Plotly, Rust, Typescript, Python or Matplotlib. Experience developing machine learning models for prediction, optimisation or classification. Strong SQL skills and experience working ...

science, machine learning, pricing analytics, or commercial operations. Strong technical background in Python or R, SQL, and applied ML libraries (e.g. scikit-learn, TensorFlow, XGBoost). Experience with data visualization tools (Tableau, Power BI, or similar). Understanding of statistical methods, supervised/unsupervised learning, and business experimentation. Ability ...

also unstructured data eg. Text, PDFs, jpgs, call recordings, video,etc. Knowledge of machine learning modelling techniques and how to fine-tunethosemodels eg. XGBoost, Deep Neural Networks, Transformers, ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. ...

SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM) Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term ...

unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface ...

tuning, memory management, and debugging skewed datasets. ML & MLOps - Strong experience with MLflow (Tracking, Model Registry, Projects), plus standard ML libraries (e.g., scikit-learn, XGBoost, LightGBM, PyTorch). Enterprise Governance - Proven hands-on experience structuring environments with Unity Catalog and Delta Lake (ACID transactions, time travel, Z-ordering, Liquid Clustering ...

Airflow, Prefect, etc.). Hands-on experience with common ML libraries and frameworks, e.g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. Clear evidence of practical experience defining and deploying containerised systems, i.e.: Implementing APIs for internal services, e.g. via FastAPI; Deploying containerised systems to production ...

datasets. Experience with, or exposure to, PySpark and Databricks. A good understanding of machine learning techniques, which could include classification, regression, gradient boosting/XGBoost, segmentation, propensity modelling or survival analysis. Experience or knowledge of production ML practices such as Git, CI/CD, MLflow, automated testing, model deployment ...

datasets. Experience with, or exposure to, PySpark and Databricks. A good understanding of machine learning techniques, which could include classification, regression, gradient boosting/XGBoost, segmentation, propensity modelling or survival analysis. Experience or knowledge of production ML practices such as Git, CI/CD, MLflow, automated testing, model deployment ...

platforms.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 ...

kept working. Statistics and experimentation: hypothesis testing, A/B testing, power and sample sizing. Classical ML: classification and ranking with scikit-learn and XGBoost or LightGBM, evaluated rigorously: validation design, calibration, leakage. Decision science: applied work with at least one of contextual bandits, causal inference or uplift modelling ...

QlikSense Experience in SQL (any variant), Alteryx, MS Excel and VBA Experience in applying various analytical techniques (e.g. naïve bayes, decision trees, random forest, xgboost and k-means), to calculate problems such as predictive forecasting, prescriptive action, capacity planning. Experience of working in teams, managing the workload of junior team ...

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 ...

Hands-on experience building credit scorecards or risk models (application, behavioural, or collections) Proficiency in Python and SQL; experience with ML frameworks (scikit-learn, XGBoost, etc.) Familiarity with Open Banking data, bureau data (Experian, Equifax, TransUnion), or alternative data sources Comfort working in small, fast-moving teams where you have ...

ranking, NLP, or LLMs).• Data & ML Ecosystem: Familiarity with data processing libraries (e.g. Pandas, NumPy) and ML frameworks (e.g. PyTorch, Scikit-learn or XGBoost).• Growth Mindset: A genuine interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.At Quantcast, we craft offers that reflect ...

techniques (e.g., Gradient Boosted Trees, Random Forests). Core Technical Proficiency: High proficiency in Python for data science (including libraries like Pandas, Scikit-Learn, XGBoost/LightGBM) and SQL. Large-Scale Data Expertise: Demonstrable experience working with very large, complex, and sometimes messy datasets to build effective models. Experience with ...

/Senior) to analyse longitudinal healthcare datasets and build predictive models. The role emphasizes Python/SQL proficiency, ML libraries (scikit-learn, pandas, XGBoost) and NLP transformer techniques, in a fast-moving, cross-functional team. You will validate and deploy models on real clinical data, address missing values and class ...

datasets. The ideal candidate should have strong Python and SQL skills and be proficient in data science libraries such as scikit-learn, pandas and XGBoost, as well as transformer-based NLP techniques. We are looking for someone who works well in a fast-moving team and communicates effectively with ...

based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) < Strong knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn ...

learning models across the full lifecycle Develop and enhance Probability of Default (PD) models and scorecards Create revenue forecasting models using techniques such as XGBoost and CatBoost Partner with business stakeholders to solve complex commercial problems Support the evolution of data science infrastructure and best practices Your Skills & Experience Strong ...

with: Data Quality Data Governance techniques Knowledge of: MLOps Feature Engineering Exposure to data analysis and ML tools such as: scikit-learn pandas NumPy XGBoost LightGBM Familiarity with orchestration tools: Apache Oozie Apache Airflow Why you’ll love it here We want to make Prima a happy and empowering place ...

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 ...