20 of 20 Permanent Feature Engineering Jobs in the UK excluding London

helping shape scalable and reliable ML systems that power smarter decisions across the business. Working closely with cross-functional teams, you'll optimise model performance, improve data preprocessing and feature engineering, and continuously enhance the accuracy and efficiency of our models. This is a hands-on role where you'll bring innovation, collaboration, and curiosity together to help … to support scalable model delivery. Continuously improve model performance through thoughtful experimentation and hyperparameter tuning. Strengthen the reliability and scalability of OVO's ML systems. Enhance data preprocessing and feature engineering to boost model accuracy and efficiency. You'll be a successful Machine Learning Engineer at OVO if you Excellent production level programming skills in Python, including experience … designing, and deploying ML pipelines in production environments; knowledge of Kubeflow Pipelines is a plus. Good understanding of ML principles, monitoring, security, and data preprocessing techniques. Familiarity with software engineering practices, such as design patterns, CI/CD, version control, containerisation, infrastructure as code/Terraform; knowledge of Kubernetes is a plus. Strong communication traits, able to explain technical More ❯

vital component of the business, and is responsible for: Responsibilities Develop, train, and deploy machine learning models for risk scoring, behavioural analytics, fraud detection and extreme event detection. Optimize feature engineering, model performance, and real-time inference pipelines for large-scale datasets. Work on supervised, unsupervised, and reinforcement learning models to enhance decision-making. Leverage telematics, mobility, and … and Data Modelling Conduct exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities. Ensure robustness and scalability of data science pipelines, minimizing bias and improving accuracy. Data Engineering and Infrastructure Collaborate with the rest of the Engineering team to integrate machine learning models into production-grade systems. Work with big data processing frameworks (Spark, AWS, Azure … to scale data pipelines. Ensure efficient data wrangling, transformation, and feature selection using Python, SQL, and distributed computing. Optimize data workflows and cloud-based machine learning architectures, ensuring efficiency and performance. Collaboration and Cross-Functional Partnerships Work closely with Product, Engineering, and Commercial teams to align data science initiatives with business goals. Collaborate with Software Engineers to deploy More ❯

the frontier of applied analytics and machine learning. 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 ❯

to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on cloud like Azure, AWS or GCP is preferred. Foundational Knowledge: A strong foundation in statistics, mathematics, AI … principles and programming is essential for success in this role. Ideally, you will also have Problem-Solving: Ability to translate business assumptions and rules into feature engineering and model explainability, addressing business problems with data-driven solutions. Collaborative Development: Work under the guidance of senior data scientists and solution architects to build models that align with strategic visions More ❯

intelligent systems and eager to contribute to real-world AI challenges. The Role This is an exciting opportunity for a highly motivated graduate to join a cutting-edge AI engineering team. You’ll work alongside experienced data scientists, researchers, and software engineers to design, build, and deploy scalable ML models that drive impactful products. The ideal candidate will combine … environment. Key Responsibilities Research, develop, and implement machine learning models for real-world applications. Collaborate with cross-functional teams to integrate ML solutions into production systems. Conduct data preprocessing, feature engineering, and model evaluation using modern frameworks. Contribute to experimentation, prototyping, and optimization of AI algorithms. Stay up to date with the latest developments in ML/AI 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 ❯

foundation in statistics, mathematics, and programming is essential for success in this role. The following will be an advantage; Problem-Solving: Ability to translate business assumptions and rules into feature engineering and model explainability, addressing business problems with data-driven solutions. Collaborative Development: Work under the guidance of senior data scientists and solution architects to build models that 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 ❯

help develop advanced machine learning models that predict and prevent equipment failures in large-scale industrial operations. This is an exciting opportunity to apply data science to real-world engineering challenges , using rich, high-frequency data from complex machinery. Their AI-driven predictive maintenance platform is built on years of operational data and designed to help clients reduce downtime … maintenance strategies. Develop and maintain data pipelines using tools like Apache Airflow for efficient workflows. Use MLflow for experiment tracking, model versioning, and deployment management. Contribute to data cleaning, feature engineering, and model evaluation processes. Collaborate with engineers and data teams to better understand equipment behavior and enhance model performance. What We're Looking For A Bachelor's … degree in Computer Science, Mathematics, Electrical Engineering, or a related field. Strong experience with Python and data science libraries (Pandas, Scikit-learn, etc.). Solid understanding of machine learning concepts and algorithms . Interest in working with real-world industrial or sensor data . Exposure to Apache Airflow and/or MLflow (through coursework or experience) is a plus. More ❯

to support real-time health assessment. Collaborate with electronics engineers to interface machine learning models with handheld and wearable sensor systems Develop pipelines for real-time data acquisition and feature extraction and evaluate model performance and system-level integration Establish rigorous data governance and pre-processing protocols to ensure data integrity, security, and compliance with healthcare standards Work closely … research findings, supporting translation into practice through collaborations, providing training and helping to secure research funding About you: PhD or equivalent industrial experience in Computer Science, Data Science, Biomedical Engineering, Applied Mathematics, or a closely related discipline with a focus on machine learning or data-driven modelling Proven expertise in developing, training, and validating machine learning and statistical models … and real-time data interpretation Demonstrated ability to integrate ML algorithms with sensor systems, or embedded hardware Proficiency in Python, MATLAB, or equivalent programming environments Experience in data curation, feature engineering, and pre-processing for multimodal healthcare or sensor datasets Strong track record of publishing research in peer-reviewed journals or writing industrial reports. Excellent communication skills, including More ❯

experience in a data science or machine learning role Strong programming skills in Python and familiarity with key data libraries (e.g. pandas, NumPy, scikit-learn) Experience with data wrangling , feature engineering, and model optimisation Understanding of data pipelines , APIs, and production deployment workflows Excellent communication skills and a collaborative approach Why Apply? Work on mission-critical UK Government More ❯

foundation in statistics, mathematics, and programming is essential for success in this role. The following will be an advantage; Problem-Solving: Ability to translate business assumptions and rules into feature engineering and model explainability, addressing business problems with data-driven solutions. Collaborative Development: Work under the guidance of senior data scientists and solution architects to build models that More ❯

our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise … break before wrapping up the day with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across Europe. No matter the 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 ❯

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 ❯

DBT, Delta Live Tables, and dimensional modelling to deliver consistent, trusted analytics. Enable advanced AI and ML use cases by building pipelines for vector search, retrieval-augmented generation (RAG), feature engineering, and model deployment. Ensure security and governance through robust access controls, including RBAC, SSO, token policies, and pseudonymisation frameworks. Develop resilient data flows for both batch and … pseudonymisation and retention policies Exposure to enabling GenAI and ML workloads by preparing model-ready and vector-optimised datasets Demonstrated capability to collaborate successfully with interested parties in data engineering, analytics, architecture, and business teams Advanced SQL skills and a keen focus on performance tuning, data integrity, and reusable compose patterns across data products What’s in it for 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 ❯

using DBT, Delta Live Tables, and dimensional modelling to deliver consistent, trusted analytics.Enable advanced AI and ML use cases by building pipelines for vector search, retrieval-augmented generation (RAG), feature engineering, and model deployment.Ensure security and governance through robust access controls, including RBAC, SSO, token policies, and pseudonymisation frameworks.Develop resilient data flows for both batch and streaming workloads … implementation of pseudonymisation and retention policiesExposure to enabling GenAI and ML workloads by preparing model-ready and vector-optimised datasetsDemonstrated capability to collaborate successfully with interested parties in data engineering, analytics, architecture, and business teamsAdvanced SQL skills and a keen focus on performance tuning, data integrity, and reusable compose patterns across data productsAdditional InformationWhat’s in it for you More ❯