1 to 25 of 114 XGBoost Jobs in the UK

anomalies. Engineer and select features for optimal model performance, leveraging domain understanding. Machine Learning & Statistical Modelling Implement both classical ML methods (regression, clustering, time-series forecasting) and advanced algorithms (XGBoost, LightGBM). Address computer vision, NLP, and generative tasks using PyTorch, TensorFlow, or Transformer-based models. Model Deployment & MLOps Integrate CI/CD pipelines for ML models using platforms like More ❯

people Willingness to travel. Want to stand out? Consulting Experience Databricks Machine Learning Associate or Machine Learning Professional Certification. Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc. Experience with deep learning frameworks like TensorFlow or PyTorch. Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time and batch processing. Experience with More ❯

Owners and Colleagues alike. Essential Experience: Extensive experience designing, developing and deploying machine learning and AI solutions in production environments Statistical modelling, machine learning (e.g. logistic regression, random forest, XGBoost, and modern deep learning techniques (e.g. transformers, transfer learning, reinforcement learning) Proven ability to lead technical direction across projects or domains Expertise in model validation, explainability, governance and ethical AI More ❯

Social network you want to login/join with: At loveholidays, we’re on a mission to open the world to everyone, giving our customers’ unlimited choice, unmatched ease and unmissable value for their next getaway. We are a super More ❯

Why loveholidays? At loveholidays, we're on a mission to open the world to everyone, giving our customers' unlimited choice, unmatched ease and unmissable value for their next getaway. Our team is the driving force behind our role as our More ❯

SHAP, LIME). Familiarity with reinforcement learning or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM MLOps: MLflow, Weights & Biases, Kubeflow, Seldon Core Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Cloud Platforms: AWS (SageMaker, S3, EC2), Google Cloud More ❯

mathematics, physics, or statistics. Experience in data science within financial services, insurance, or e-commerce (not essential). Familiarity with cloud-based deployment. Knowledge of neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, or Pandas. Experience with API development. Proficiency in SQL. Background in software engineering or DevOps/MLOps. Understanding of CI/CD pipelines. Why Join Us? Join a forward More ❯

mathematics, physics, or statistics. Experience in data science within financial services, insurance, or e-commerce (not essential). Familiarity with cloud-based deployment. Knowledge of neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, or Pandas. Experience with API development. Proficiency in SQL. Background in software engineering or DevOps/MLOps. Understanding of CI/CD pipelines. Why Join Us? Join a forward More ❯

deploying ML and AI solutions in production Proficiency in Python (e.g. Scikit-learn, TensorFlow, PyTorch) and SQL Expertise in statistical modelling and advanced machine learning (e.g. transformers, reinforcement learning, XGBoost) Strong understanding of model validation, explainability and responsible AI practices Excellent communication and leadership skills, with the ability to influence across teams Familiarity with MLOps tooling and scalable cloud platforms More ❯

quantitative field Deep theoretical knowledge of statistical methods and ML algorithms and their practical applications. Strong proficiency in SQL and Python, especially with core ML libraries (e.g. scikit-learn, XGBoost, SciPy, PyTorch) Extensive hands-on experience in taking advanced statistical/ML solutions from prototype to production and delivering high-impact outcomes to complex business problems Proactive technical leader with More ❯

and marketing funnel analysis Proficiency in Python with extensive experience in data science & AI libraries (e.g.scikit-learn, pandas, NumPy, SciPy, etc) Experience with ML frameworks such as TensorFlow, PyTorch, XGBoost, LightGBM, or similar Strong SQL skills and experience with data warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML services (SageMaker, Azure ML, Vertex More ❯

experience in the following skills: Core Data Science & Machine Learning Strong professional experience in data science, machine learning, and business analytics Hands-on experience with ML techniques such as XGBoost, deep neural networks, and transformers Practical knowledge of ML libraries and frameworks such as Fastai, Keras, TensorFlow, PyTorch, and Scikit-learn Ability to research, understand, and apply emerging machine learning More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

media, and paid media. Excellent problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn More ❯

working with structured and also 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, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding More ❯

data warehousing, and big data tools (e.g., PySpark, Hive) • Skilled at working with structured and unstructured data (e.g., text, PDFs, audio, video) • Familiar with Machine Learning (ML) models like XGBoost, deep neural networks, transformers, Markov chains • Experienced in using Machine Learning (ML) libraries such as scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face Cloud & Deployment • Hands-on experience with AWS, Azure More ❯

of data science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD Diversity and flexible working at Hiscox At Hiscox More ❯

of data science in finance, insurance or Ecommerce is an advantage but not required ??Experience of deployment in a cloud environment ??Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas ??Experience with API development ??SQL experience ??Software engineering experience ??DevOps/MLOps experience ??Good working understanding of CI/CD If you hold the experience and technical skills More ❯

stakeholders Experience of proactively contributing to the design, development, testing, and deployment of data science and AI solutions Experience and understanding of applied machine learning techniques in Python (e.g., xgboost, regression, decision trees) Experience with physics modelling highly desirable Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g., reinforcement learning, deep learning) Experience of working More ❯

stakeholders Experience of proactively contributing to the design, development, testing, and deployment of data science and AI solutions Experience and understanding of applied machine learning techniques in Python (e.g. xgboost, regression, decision trees) Experience with computational simulation/modelling highly desirable Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g. reinforcement learning, deep learning) Experience More ❯

Contribute to UI components using React.js for interactive AI applications. Required Skills & Experience 5+ years of hands-on AI/ML development using Python. Strong knowledge of: ML algorithms: XGBoost, SVM, Random Forests, Logistic Regression Deep learning models: CNNs, RNNs, Transformers (BERT, GPT) Unsupervised learning: K-Means, PCA Experience with: RAG architecture, LangChain, and advanced prompt engineering Vector search techniques More ❯

ll bring Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Solid understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc), state-of-the-art research area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of SQL and Python's ecosystem for More ❯

Experienced working with structured and 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, etc. Must demonstrate the capacity More ❯

tasks, tracking progress, sharing ideas, and collaborating. Engaging with partners to meet their requests. Additional skills we value: Azure Machine Learning Services, Cognitive Services, Responsible AI Dashboard Python, SKLearn, XGBoost TensorFlow, PyTorch Model deployment options like Azure Functions, FastAPI, Kubernetes CI/CD pipelines, Git actions Infrastructure as code: Terraform, ARM Templates, Databricks Asset Bundles Advanced ML techniques: Graph processing More ❯