12 of 12 Remote XGBoost Jobs in the UK

ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). More ❯

ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built, and we 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 ❯

for a talented OPS Engineer to join our growing teams in Erskine or Newcastle. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with More ❯

UK What you’ll do Research, prototype, and deploy LLM use cases (Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you’ll bring Hands-on Python for data science 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 ❯

and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and behavioural models using Python and frameworks such as XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing … 4+ years of hands-on experience in applied machine learning (preferably in financial services or another regulated domain). Proven ability to design, train, and evaluate models using Python, XGBoost, and related ML frameworks. Strong experience with SQL and BigQuery; familiarity with GCP infrastructure. Comfortable working end-to-end from data exploration through validation and interpretation. Understanding of explainable AI More ❯

and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and behavioural models using Python and frameworks such as XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing … 4+ years of hands-on experience in applied machine learning (preferably in financial services or another regulated domain). Proven ability to design, train, and evaluate models using Python, XGBoost, and related ML frameworks. Strong experience with SQL and BigQuery; familiarity with GCP infrastructure. Comfortable working end-to-end from data exploration through validation and interpretation. Understanding of explainable AI More ❯

and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and behavioural models using Python and frameworks such as XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing … 4+ years of hands-on experience in applied machine learning (preferably in financial services or another regulated domain). Proven ability to design, train, and evaluate models using Python, XGBoost, and related ML frameworks. Strong experience with SQL and BigQuery; familiarity with GCP infrastructure. Comfortable working end-to-end from data exploration through validation and interpretation. Understanding of explainable AI More ❯

and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and behavioural models using Python and frameworks such as XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing … 4+ years of hands-on experience in applied machine learning (preferably in financial services or another regulated domain). Proven ability to design, train, and evaluate models using Python, XGBoost, and related ML frameworks. Strong experience with SQL and BigQuery; familiarity with GCP infrastructure. Comfortable working end-to-end from data exploration through validation and interpretation. Understanding of explainable AI More ❯

and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and behavioural models using Python and frameworks such as XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing … 4+ years of hands-on experience in applied machine learning (preferably in financial services or another regulated domain). Proven ability to design, train, and evaluate models using Python, XGBoost, and related ML frameworks. Strong experience with SQL and BigQuery; familiarity with GCP infrastructure. Comfortable working end-to-end from data exploration through validation and interpretation. Understanding of explainable AI More ❯

team members. Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with … e.g., Git). Excellent problem-solving skills and ability to work independently or in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with More ❯