12 of 12 Permanent Feature Engineering Jobs in the UK excluding London

frontier of applied analytics and machine learning. Key Responsibilities: Contribute to the development of predictive and statistical models addressing business-critical challenges across diverse domains. Conduct exploratory data analysis, feature engineering, and hypothesis testing to uncover patterns and support model development. Collaborate with senior data scientists and ML engineers to refine models, improve accuracy, and enhance interpretability. Support More ❯

applications. In this role, you will focus on hands‐on data science tasks, such as designing experiments, gathering and preprocessing data, building and evaluating models, and collaborating closely with engineering teams to deploy production‐ready solutions. Ideal candidates should be proficient in Python (Jupyter Notebooks), familiar with machine learning frameworks like TensorFlow or PyTorch, and experienced in analyzing large … New Zealand, UK or Australia. Have a strong background in one or more of the following areas: exploratory data analysis and statistical inference, machine learning workflows and model evaluation, feature engineering/data preprocessing/data wrangling, or A/B testing/experimentation/causal inference. Demonstrate excellent verbal and written communication skills. Have strong attention to More ❯

customer behaviours (e.g., lifetime value, acquisition efficiency), providing critical insights to our marketing and commercial teams. Own the complete development lifecycle for your machine learning models, from data gathering, feature engineering, and POC to model training, validation, and production deployment. Partner with our Machine Learning and Software Engineering teams to deploy your models as scalable, reliable, and More ❯

at its core their platform uses machine learning to turn business insights into business success. As a Machine Learning Engineer, you'll sit at the intersection of data science, engineering, and commercial strategy, working closely with our product and core engineering teams to translate business challenges into deployable ML solutions. You'll have the autonomy to experiment, iterate … gained working In a mission focussed, product led business. In return this ML Engineer will receive extensive growth and personal development opportunities as the business transitions away from legacy engineering practices. This ML Engineer based near Birmingham should have most of the following key skills: - Demonstrated experience delivering business impact and growth through ML solution design and development - Proven … experience in Python, TensorFlow/PyTorch, and modern ML frameworks. - Strong background in data modelling, feature engineering, and model deployment. - Experience with SQL, cloud platforms (AWS/GCP/Azure), and API integration. - A commercial mindset - you think in terms of ROI, not just model accuracy. - Excellent communication skills with the ability to influence non-technical stakeholders. - Experience More ❯

Engineer/Data Modeller role offers the chance to make a significant impact within an innovative, tech-driven organisation. You'll work at the forefront of data modelling, analytics engineering, and AI-enablement, helping shape scalable data products that power enterprise reporting, machine learning, and self-service insights. Senior Analytics Engineer/Data Modeller Responsibilities Design, build, and maintain … learning. Ensure documentation, governance, and data consistency across domains. Collaborate with data engineers to support robust ETL/ELT pipelines and maintain end-to-end data lineage. Deploy analytics engineering solutions using dbt, SQL transformations, and CI/CD best practice. Partner with analysts and data scientists to deliver performant and reliable datasets. Implement data quality monitoring, validation processes … and performance optimisation. Support AI initiatives by designing AI marts, feature engineering structures, and ML-ready datasets. Contribute to standardised KPIs, metadata, and a single source of truth for reporting. Work with business teams to translate complex requirements into scalable, high-impact models. Support agile delivery of data products and continuous improvement of modelling standards. Senior Analytics Engineer More ❯

Scientist to lead the development and application of advanced analytics and machine learning techniques that answer complex business questions and drive strategic decisions. You'll work closely with data engineering and business teams to design models and analytical solutions that improve customer experience, optimise operations, and support financial and strategic planning.This role combines technical expertise with leadership responsibilities, including … Mentor team members, present insights clearly to technical and non-technical audiences, and ensure best practices in analytical workflows. Technical Delivery: Engineer and optimise analytical and spatial SQL, build feature enrichment pipelines, develop and evaluate ML models, and package outputs for deployment. What You'll Need to Succeed Strong experience in applied machine learning (regression, tree ensembles, experiment design … . Advanced proficiency in Python and SQL, with experience in spatial SQL and PostGIS. Familiarity with spatial tools and libraries (GeoPandas, QGIS) and feature engineering concepts. Experience with data modelling, dbt, and version control (Git). Knowledge of spatial datasets (MasterMap, AddressBase, Land Registry). Desired: Experience with WMS/WFS services, graph theory (NetworkX), GDAL, and Snowflake. More ❯

the structure and purpose of their outputs and determine the most effective ways to visualise and communicate them. Data Product Development Support the development of robust data pipelines and feature engineering activities in partnership with Data Engineers. Design and develop a unified, user-friendly front-end interface that integrates dashboards, reports, models, and pipeline outputs into a single … or equivalent). Solid understanding of UX principles and accessibility standards (WCAG 2.2). Ability to collaborate with multidisciplinary teams and communicate complex information effectively. Desirable Experience with data engineering concepts such as pipelines, orchestration, or feature engineering. Familiarity with component-driven design systems. Knowledge of performance optimisation techniques for front-end applications. Experience working in Agile or More ❯

Data Science team. Key Responsibilities: Analyse datasets to identify patterns, trends, and actionable insights. Support the development and evaluation of machine-learning models. Assist with data cleaning, preparation, and feature engineering. Conduct statistical analysis to support business decisions. Build reports, dashboards, and visualisations for internal stakeholders. Collaborate with technical and non-technical teams to translate data into impact. Stay More ❯

hands-on environment. Key Responsibilities: Analyse datasets to identify patterns, trends, and actionable insights. Support the development and evaluation of machine-learning models. Assist with data cleaning, preparation, and feature engineering. Conduct statistical analysis to support business decisions. Build reports, dashboards, and visualisations for internal stakeholders. Collaborate with technical and non-technical teams to translate data into impact. Stay More ❯

in applied deep learning, ideally in domains involving high-frequency or large-scale time-series data. You will focus on developing alpha signal generation pipelines from data ingestion and feature engineering to model training and deployment - in collaboration with our trading and infrastructure teams. Responsibilities: Develop ML-based alpha generation models using high-frequency order book and market … microstructure data. Design and maintain data pipelines, preprocessing, and feature extraction workflows tailored to streaming tick data. Research and implement advanced deep learning architectures for short-horizon forecasting and signal extraction. Collaborate with quant researchers and developers to integrate models into live trading environments. Optimise inference latency and robustness; ensure models behave safely under live market conditions. Continuously refine More ❯

efficient planning and delivery capabilities. Experience working with a range of data types, including structured and unstructured data. Possess a broad knowledge of data science methodologies/techniques (e.g. feature engineering, modelling, evaluation), with ability to implement best practise solutions according to the demands of a project. Has expertise of a narrower range of specialisms (e.g. LLMs or More ❯

work. You'll gain a better understanding of the diverse array of challenges we consider every day, learning how we think about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets. Your day-to-day project work will be complemented by classes on the broader fundamentals of markets and trading, lunch seminars More ❯