14 of 14 Remote/Hybrid Feature Engineering Jobs in the UK excluding London

hire has very meaningful impact.They are looking for a Senior Data Scientist to lead the predictive analytics capability and their critical programme.You’ll design feature engineering pipelines, select and validate classification and regression models, and establish rigorous evaluation methodology (targeting AUC-ROC 0.85) across a 300M+ point education … explainable and production-ready.Core Responsibilities: Predictive Model Development : Build and validate supervised learning models (classification & regression) using complex, multi-source, time-series-influenced data. Feature Engineering : Design scalable feature pipelines across attendance, behaviour, wellbeing, assessment and SEND datasets. Evaluation & Methodology : Establish rigorous evaluation frameworks including ...

solutions and regulatory reporting — ensuring teams across the business can trust and use data with confidence. You’ll collaborate closely with colleagues in Data Engineering, Data Science, Pricing, Platform Engineering and MLOps, acting as a trusted partner and technical authority. Together, you’ll define clear standards … self‐service analytics and AI/ML model consumption. Collaborate with ML Ops and Data Science teams to define data product standards and feature engineering patterns. Evaluate and lead adoption of emerging Azure and Databricks capabilities — including Microsoft Fabric, OneLake and DirectLake — where they advance the data architecture. ...

solutions and regulatory reporting — ensuring teams across the business can trust and use data with confidence. You’ll collaborate closely with colleagues in Data Engineering, Data Science, Pricing, Platform Engineering and MLOps, acting as a trusted partner and technical authority. Together, you’ll define clear standards … self‐service analytics and AI/ML model consumption. Collaborate with ML Ops and Data Science teams to define data product standards and feature engineering patterns. Evaluate and lead adoption of emerging Azure and Databricks capabilities — including Microsoft Fabric, OneLake and DirectLake — where they advance the data architecture. ...

deploying scalable machine learning models that drive data-driven decision-making across the business. This role bridges the gap between data science and software engineering, turning prototypes into production-ready systems. Key Responsibilities Design, develop, and deploy machine learning models and pipelines in production environments Collaborate with data scientists … software engineers, and stakeholders to translate business requirements into ML solutions Optimize model performance, scalability, and reliability Build and maintain data pipelines and feature engineering workflows Monitor and retrain models to ensure continued performance over time Implement best practices for version control, testing, and CI/ ...

priorities. Identify high-value ML use cases and translate commercial problems into scalable analytical solutions. Lead end-to-end model lifecycle delivery: problem framing, feature engineering, experimentation, validation, deployment, monitoring, and iteration. Establish best practices for experimentation, evaluation, reproducibility, and documentation. Set standards for model governance, explainability, monitoring … financial environment. Machine Learning & Delivery Architect and develop robust ML models (e.g., classification, regression, anomaly detection) using Python and cloud-based tooling. Design scalable feature pipelines in collaboration with Data Engineering. Lead productionisation efforts, including pipeline design, model versioning, and monitoring frameworks. Implement safe deployment strategies (e.g., champion/ ...

Collaborate with data engineers, analysts, and product teams to translate business needs into AI-driven solutions Contribute to the development of data pipelines and feature engineering workflows Integrate models into production using APIs, batch jobs, or real-time systems Apply best practices around experimentation, evaluation, versioning, and monitoring ...

problem definition through to deployment in live environments. * Design, build, and deploy machine learning models within Pega-based decisioning platforms. * Lead model development across feature engineering, training, validation, and ongoing performance monitoring. * Work closely with application and logic developers to embed models into operational decision workflows. * Develop statistical ...

emphasis is on refinement, optimisation and continuous improvement rather than building from scratch. You will be responsible for the intellectual core of the models: Feature engineering across financial, behavioural and transactional data Algorithm selection and tuning (logistic regression, gradient boosting, ensemble methods) Model validation, performance optimisation and ongoing … recalibration Ensuring models remain robust in changing economic conditions You will not be responsible for infrastructure, pipelines or deployment. A dedicated engineering team manages AWS and production environments, allowing you to focus on modelling and analytics. This is a highly visible role with direct exposure to senior stakeholders. ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … doDevelop and maintain the machine learning models which define our competitive advantage in the financial services market.Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation.Analyse tabular and non-tabular data, such as text, logs, or time series ...