17 of 17 XGBoost Jobs in the UK

6+ years of experience in ML Engineering or Data Science (finance, fintech, or treasury a plus). Proficiency in Python-including pandas, scikit learn, TensorFlow/PyTorch, LightGBM/XGBoost-and experience with SQL. Hands on experience with cloud ML platforms (AWS SageMaker, Azure ML, or Google AI Platform). Solid understanding of software engineering fundamentals: version control, code reviews More ❯

Experience developing & deploying productionised Machine Learning applications on a cloud platform (GCP ideal, AWS & Azure also acceptable) * Experience with common Python packages for Machine Learning - examples include PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas, etc. * Strong knowledge of SQL and its use for data preparation & feature engineering * Understanding of & practical experience with implementing MLOps principals - including automated model retraining 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 ❯

solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI/CD More ❯

solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI/CD More ❯

solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. Experience with CI/CD More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written and verbal communication More ❯

latent class, hierarchical, and factor analysis. Understanding of working with market research and customer databases. Knowledge of statistics and machine learning applications. Experience with predictive modeling using linear models, XGBoost, discriminant analysis, etc. Beneficial Skills Experience with conjoint analysis and other pricing methods. Knowledge of GDPR and data compliance. Experience working with third-party data vendors and APIs. Some familiarity More ❯

services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning Engineer, timeseries, forecasting, VertexAI More ❯

Strong knowledge of R and relevant data science packages (e.g. caret, h2o, mlr). Strong knowledge of Python and the relevant data science Python stack (Numpy, Scikit-learn, Scipy, Xgboost). Desirable An understanding of, or experience working in, the insurance and/or actuarial market would be highly advantageous. Desirable Knowledge and understanding of actuarial science, and risk. Desirable More ❯

high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn, XGBoost, or LightGBM Strong SQL skills for data extraction and transformation Experience working with transactional (e.g., Open Banking) and bureau data (e.g., Experian, Equifax) Expertise in feature engineering, handling class imbalance More ❯

high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn, XGBoost, or LightGBM Strong SQL skills for data extraction and transformation Experience working with transactional (e.g., Open Banking) and bureau data (e.g., Experian, Equifax) Expertise in feature engineering, handling class imbalance More ❯

multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models. BASIC QUALIFICATIONS - 2+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or More ❯

Strongknowledgeofdatamodelling,machinelearning,and/oradvanceddataanalytics. Demonstrabletrackrecordofdeliveringdataanalyticsprojectsaspartofateam. Hands-onexperiencewithcollaborativesoftwaredevelopmentandversioncontrol(preferablyGit). FamiliaritywithAgile/SCRUMmethodologies. Exposuretopre-engagementactivitiessuchasprojectscoping,technicalfeasibilityanalysis,orprototypedevelopment. Comfortablecontributingtotechnicaldiscussionsandimplementingsolutionsdefinedbyprojectleads. DesirableExperience StrongPythonexpertise. ExperiencewithGNU/Linuxenvironments. FamiliaritywithkeydatascienceandMLframeworks(e.g.,scikit-learn,PyTorch,TensorFlow,XGBoost,HuggingFace). Experienceinnaturallanguageprocessing,tabulardataanalysis,orcomputervision. SQLproficiency. Exposuretocontainerisation(Docker,Kubernetes)andcloud-nativearchitectures. ExperiencewithCI/CD,automatedtesting,anditerativeproductdevelopment. Knowledgeofgraphdatabasesandgraphanalysis. Benefits Ourclientoffersanexcitingandsupportiveenvironmentwithastrongfocusonemployeewellbeingandcareerdevelopment.Benefitsinclude: 35daysannualleave(includingpublicholidays)plusupto10daysunpaidleave. Flexibleworkingarrangementsaroundcorehours. Privatehealthinsuranceandpensionscheme. Contributiontogymmembership. Ongoingprofessionaldevelopmentsupport(courses,certifications,conferences). More ❯