1 to 25 of 223 Permanent XGBoost Jobs in England

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 ❯

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 ❯

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 ❯

data-driven decision-making throughout the organisation. Required Skills & Experience: Extensive experience conducting data analysis within relevant sectors. Proficiency in SQL, Python, and data science libraries such as sklearn, XGBoost, and CatBoost. Experience with Azure, MLOps, and API frameworks. Strong understanding of statistical methods, probability theory, pricing optimisation, and elasticity modelling. Expertise with data visualisation tools (e.g., Tableau). Exceptional 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 ❯

deliverables Qualifications 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 areas (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 ❯

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 ❯

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 ❯

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 ❯

modelling, forecasting, and optimization. Strong proficiency in Python, SQL, and cloud-based data platforms (mainly Google Cloud and AWS). Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn, XGBoost). Strong stakeholder management skills, with the ability to communicate findings to non-technical teams and influence business strategy and commercial outcomes. Hands-on experience with pricing models, demand forecasting 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 ❯

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 ❯

with stakeholders to translate business challenges into data-driven solutions. Requirements: MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields. Experience in ML algorithms (XGBoost, clustering, regression) Expertise in Time Series, NLP, Computer Vision, MLOps Knowledge of AWS/Azure/GCP, CI/CD, and Agile development Ability to own solutions and manage stakeholders More ❯

data-driven decision-making throughout the organisation. Required Skills & Experience: Extensive experience conducting data analysis within relevant sectors. Proficiency in SQL, Python, and data science libraries such as sklearn, XGBoost, and CatBoost. Experience with Azure, MLOps, and API frameworks. Strong understanding of statistical methods, probability theory, pricing optimisation, and elasticity modelling. Expertise with data visualisation tools (e.g., Tableau). Exceptional More ❯

/AI ecosystem and bring fresh ideas to the table What we’re looking for: Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments More ❯

/AI ecosystem and bring fresh ideas to the table What we’re looking for: Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments More ❯

data science projects and deliverables Qualifications Strong understanding of computer science fundamentals: data structures, algorithms, data modeling, software architecture Experience with classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost), research areas (e.g., NLP, Transfer Learning), and Deep Learning (e.g., BERT, LSTM) Proficiency in SQL and Python (Jupyter, Pandas, Scikit-learn, Matplotlib) Knowledge of model evaluation, data preprocessing techniques, and More ❯

/AI ecosystem and bring fresh ideas to the table What we’re looking for: Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience working with structured data (credit, payments 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 ❯

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 ❯

science or machine learning roles, preferably in a fast-paced tech or enterprise environment. Proficient in Python or R, SQL, and common machine learning frameworks (e.g., scikit-learn, TensorFlow, XGBoost, PyTorch). Strong foundation in statistics, probability, and hypothesis testing. Experience with cloud platforms (AWS, GCP, Azure) and big data tools (Spark, Hive, Databricks, etc.) is a plus. Excellent communication More ❯

with developments in the ML/AI ecosystem and bring fresh ideas to the table Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., Experience working with structured data (credit, payments, customer behaviour) and applying feature engineering 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 ❯