11 of 11 Feature Engineering Jobs in the South East

Work with large datasets to build predictive and analytical models • Collaborate with data scientists to transition models from research to scalable production systems ML Engineering and Deployment • Design and maintain machine learning pipelines and model deployment infrastructure • Implement automated model training, validation, and monitoring processes • Ensure models are scalable … reliable, and performant in production environments Data Engineering and Infrastructure • Work with data engineers to build efficient data pipelines and feature engineering processes • Optimise data workflows to support model development and experimentation • Contribute to the development of robust ML infrastructure and tooling Collaboration and Product Integration • Work ...

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 ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

across the business. They are looking for a Senior AI Engineer to lead the delivery of end-to-end AI products, working across software engineering, machine learning, and LLM-based systems. This is a hands-on role focused on shipping real solutions into production that directly impact customers, teams … commercial outcomes. The RoleYou will join a cross-functional AI function working at the intersection of software engineering, data, and machine learning. Your focus will be on turning business problems into production-ready AI systems, from early discovery through to deployment, monitoring, and continuous improvement. You will work closely ...

adapted across multiple operational entities globally. Key Responsibilities Design and implement predictive and prescriptive models to optimize MRO operations. Perform exploratory data analysis, feature engineering, and model validation. Collaborate with data engineering teams to ensure high-quality, production-ready datasets. Communicate findings and recommendations to senior business ...

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 ...

Analyse business processes with stakeholders to identify automation opportunities and define clear requirements. Develop automation scripts and build data pipelines covering ingestion, preprocessing and feature engineering. Conduct testing, troubleshooting and performance monitoring to maintain system accuracy and reliability. Collaborate with data scientists, software engineers and IT teams to ensure ...

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 ...

skills, and a results-driven mindset Role responsibilities: Build, tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

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 ...