16 of 16 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 ❯

AI/ML features, model selection, architecture decisions Conduct exploratory data analysis and communicate insights to stakeholders Collaborate with data engineers, architects, and business analysts on integrated solutions Build feature engineering pipelines and automated data preparation workflows Design AI solutions for commercial transformation including pricing optimization, customer segmentation, and revenue management Develop scalable AI/ML pipelines on … including supervised, unsupervised, and reinforcement learning approaches Strong proficiency in statistical modeling, time-series forecasting, and predictive analytics Experience with deep learning frameworks (TensorFlow, PyTorch, Keras) Knowledge of prompt engineering, RAG (Retrieval Augmented Generation), and LLM fine-tuning techniques Understanding of natural language processing, computer vision, and recommender systems Essential: Hands-on experience with at least one of: Databricks … procurement analytics Commercial Transformation: Price optimization, customer lifetime value, churn prediction, marketing mix modeling Preferred Qualifications Advanced degree (Master's or PhD) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field Big 4 or tier-1 consulting firm experience Certifications such as: Azure AI Engineer Associate or Data Scientist Associate Experience with generative AI platforms (Azure More ❯

engineering. Design and implement AI-powered solutions, leveraging Python, SQL/NoSQL, and APIs for seamless integration. Prototype and rigorously test AI applications, focusing on improving performance through strategic feature engineering and model optimization. Engage with the latest open-source AI/ML tools to enhance business solutions continuously. Develop, deploy, and manage machine learning models aimed at … of various ML algorithms including supervised, unsupervised, and deep learning. Strong problem-solving and effective communication skills. Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Engineering, or related field. Benefits: 25 days annual leave plus bank holidays Performance-based bonus scheme linked to individual, team, and company achievements Generous Defined Contribution Pension scheme Permanent Health More ❯

teams to automate workflows, monitor performance, and retrain models as needed. You'll bring a passion for building reliable ML infrastructure, a strong technical foundation in modern machine learning engineering, and a track record of working in environments where reliability and scale are paramount. Our Data & Analytics team has grown significantly over the last 18 months, with strong ongoing … ML pipelines for training, deployment, monitoring, and retraining at scale. Working with data scientists to take models from development to production-grade systems, ensuring scalability, reproducibility, and robustness. Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using … Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to deliver ML features that directly impact customer experience. Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs). Monitoring deployed solutions in 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 ❯

quantitative models and metrics, extend, and maintain quantitative models, metrics and investment frameworks, with rigorous back‐testing, scenario design, and attribution . Integrate new indicators and alternative datasets; formalise feature engineering and signal decay/robustness analysis; implement model risk controls , documentation, and reproducibility. Scale and commercialise proprietary metrics for investment use‐cases and new revenue lines (e.g. … in Physics/Mathematics/Computer Science/Quantitative Finance or related STEM field (exceptional MSc considered with relevant work experience). 4–7 years in quantitative research/engineering within capital markets; proven delivery of research to production quant solutions at scale. Demonstrable experience with large scale datasets, data engineering workflows, and production automation. Python (production grade … SQL, Linux; strong software engineering (testing, type hints, code review, packaging). Cloud (AWS/Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

or a related field. Proficiency in programming languages such as Python, R, or similar, along with experience with machine learning frameworks. Strong understanding of statistical analysis, data processing, and feature engineering. Experience of working in a Consultancy and being client-facing is beneficial Security Clearance is required for this vacancy. Candidates will need to have DV Clearance. Reasonable Adjustments More ❯

talented, collaborative and enthusiastic group who use our expertise to derive insights from complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. What you'll do Develop … and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time series, to produce powerful new insights. Consult on complex statistical test … and extract actionable solutions, working cross-functionally to support key business initiatives and drive sustainable growth. What we're looking for A strong understanding of probability, statistics, machine learning, feature extraction and familiarity with large data set manipulation. Experience using deep learning models, particularly for sequential data. Experience working with Open Banking or Credit Bureau data. Experience working with More ❯