analysis and forecasting techniques. Strong foundation in computer science principles - data structures, algorithms, software architecture, and data modelling. Deep understanding of machine learning algorithms including but not limited to LogisticRegression, Random Forest, XGBoost. Familiarity with modern deep learning approaches such as BERT, LSTM, and concepts in NLP and Transfer Learning. Reasonable Adjustments: Respect and equality are core More ❯
developing GCP machine learning 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. LogisticRegression, 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. More ❯
defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logisticregression models as a minimum. More ❯
defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logisticregression models as a minimum. More ❯
defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logisticregression models as a minimum. More ❯
london (city of london), south east england, united kingdom
ONMO
defining the foundations and best practices to allow automation and smart solutions for our customers. Tools experience that is beneficial: Python (required), SQL, Power BI. Good experience using XGBoost & Logisticregression models as a minimum. More ❯
developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logisticregression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Fintellect Recruitment
developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logisticregression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯
london, south east england, united kingdom Hybrid / WFH Options
Fintellect Recruitment
developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logisticregression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯
london (city of london), south east england, united kingdom Hybrid / WFH Options
Fintellect Recruitment
developing insightful management information (MI) and presenting data-driven narratives. Effective time management and prioritisation skills. Experience with Python (required), SQL, and Power BI. Practical experience with XGBoost and logisticregression models (minimum). Benefits Private Medical Care: Access to premium healthcare services. Health Cash Plan: Contributions toward everyday medical expenses, including digital physio and skin clinic access. More ❯