8 of 8 Permanent Feature Engineering Jobs in the UK excluding London

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 ❯

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 ❯

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 ❯

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 ❯

data science and AI modelling solutions to help solve business problems. What you'll do Work with the team to execute data science projects, including data collection, pre-processing, feature engineering, model development, validation, and deployment. Engage with business stakeholders to understand domain-specific challenges and opportunities, and collaborate during development to ensure alignment with requirements. Apply advanced … classification, clustering, time series analysis, natural language processing, and deep learning. Create clear and compelling visualisations to communicate complex analytical findings effectively. Collaborate with stakeholders (including business, product, and engineering) to understand requirements, identify opportunities, and provide data-driven recommendations. Collaborate with cross-functional teams to integrate data science solutions into products and services. Actively participate in knowledge sharing 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 ❯

Mastering and deploying robust Retrieval-Augmented Generation (RAG) pipelines, integrating LLMs with complex enterprise data sources for unparalleled accuracy. - END-TO-END DELIVERY: Owning the entire solution lifecycle, from feature engineering and data mapping to scalable, production-ready deployment. - CLOUD EXCELLENCE: Working closely with Data Engineers to utilize and optimize the full potential of the [Cloud Environment] ecosystem. … familiarity with the [Specific Vendor]’s [Cloud Environment] AI and Data Stack (Azure ML, Azure OpenAI, Databricks). - SPECIALIZATION: Expertise in Vector databases (FAISS, Pinecone, Azure AI Search), Prompt Engineering, and familiarity with products like [Specific Vendor Product 1]. THE CULTURE Our client seeks curious, pragmatic problem-solvers who embody their core values: Integrity, Drive, Empathy, Adaptability, and 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 ❯