4 of 4 Remote/Hybrid Feature Engineering Jobs in the South East

pure management role. They are looking for a strong Data Scientist first, someone comfortable across the full lifecycle from ingestion and feature engineering through to modelling and deployment. Their data estate has evolved over time and is currently spread across multiple siloed systems with differing structures and standards. … systems, and proactively define a clear data and AI roadmap. You will lead a Data team of 5-6 across Data Science and Data Engineering, raising standards while still contributing directly to predictive models and AI driven tools. This is a genuine opportunity to bring structure, clarity and technical ...

identify automation opportunities and define technical and functional requirements. Coding and Programming: Develop automation scripts and build pipelines for data ingestion, preprocessing and feature engineering using languages such as Python, Java or R. Testing and Maintenance: Conduct thorough testing, resolve issues, monitor accuracy and system performance, and implement ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

platform. The Opportunity This is a hands-on leadership role where you will lead a team of 5–6 across Data Science and Data Engineering while remaining technically involved in modelling, machine learning deployment and architectural decisions. The platform runs on AWS and includes large-scale crawlers ingesting significant … Designing and improving a scalable AWS based data platform Rationalising fragmented data across multiple systems Leading the full machine learning lifecycle from ingestion and feature engineering through to deployment Improving neural network and rule based matching systems used to identify the same entities across different sources Embedding robust ...