17 of 17 Permanent Feature Engineering Jobs in the UK excluding London

Machine Learning, Artificial Intelligence, Predictive Analytics, Statistical Modelling, Python, SQL, PySpark, Azure, Databricks, MLflow, GitHub Actions, CI/CD, Time Series Forecasting, Propensity Modelling, Feature Engineering, Data Engineering, Azure Machine Learning, Azure Data Platform, Experiment Design, A/B Testing, Hypothesis Testing, Predictive Modelling, Customer Analytics, Marketing … that improve customer outcomes, enhance commercial performance & support strategic business decision making. Own the complete analytical lifecycle, from initial problem definition & exploratory analysis through feature engineering, model development, deployment, operational monitoring & continuous optimisation. Build sophisticated statistical & machine learning models covering areas such as: * Customer Lifetime Value * Customer Propensity ...

systems: devices that operate autonomously, make decisions in real time, and remain reliable under the constraints that define actual field deployment. The Challenge The engineering challenge is significant. You will be responsible for the complete lifecycle of machine learning models that run on embedded devices. This means: From Sensor … transform this stream into a robust, real-time classification model that runs on hardware with severe constraints on memory, computation, and power. Engineering Under Constraint. Every machine learning decision you make has downstream consequences for embedded systems. A model that requires 512MB of RAM will ...

operate reliable, secure, and observable data pipelines and curated datasets that power enterprise reporting, analytics, and AI/ML use cases.Lead AI/ML engineering as a core workstream, designing feature-ready datasets, model pipelines, and AI-ready data products, and applying engineering rigour (testing, versioning, observability … pipelines.Engineer data products and pipelines that support LLM and generative AI use cases, including retrieval-ready data structures and pipelines feeding AI applications.Drive engineering automation as a standing discipline, evaluating and adopting AI-assisted code generation and testing tools to reduce manual engineering effort, and building internal tooling ...

science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation …/ML solutions aligned to Army training objectives and enterprise architecture principles. Support the development and maintenance of ML pipelines including data preprocessing, feature engineering, model training, validation, and performance evaluation. Support integration of AI capabilities into secure operational environments, ensuring compatibility with DevSecOps pipelines and platform constraints. ...

systems behind ad relevance, bid and yield optimization, and real-time, low-latency ad serving. This is a senior, hands-on ML engineering role spanning modelling, large-scale training, and production infrastructure — you’ll take models from idea through to serving at scale, working in Spark, Python, and Java. … automation to move faster, raise the bar, and scale their impact.We value your AI skills if you have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks — and can describe projects where you shipped real work with ...

OVERVIEW:This role exists to provide technical leadership within the data engineering team, setting the standard for engineering excellence while remaining deeply hands-on. The Lead Data Engineer drives the design and delivery of scalable, resilient data platforms and pipelines that underpin Gymshark's ambition … Technical Leadership: Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team. Set and enforce engineering standards: code quality, testing, observability, security, and documentation, ensuring the whole team operates to them Lead technical discovery, design sessions, and code reviews, providing ...

bring them to life, owning the journey from concept to deployment and beyond.Own end-to-end delivery of data science solutions—from problem framing, feature engineering, monitoring, evaluation and deploymentDesign and execute hypothesis-driven analysis and statistical testing (e.g., experiment design where applicable, assumption checking, uncertainty quantification … bring them to life, owning the journey from concept to deployment and beyond.Own end-to-end delivery of data science solutions—from problem framing, feature engineering, monitoring, evaluation and deploymentDesign and execute hypothesis-driven analysis and statistical testing (e.g., experiment design where applicable, assumption checking, uncertainty quantification ...

across health and safety, sustainability and ethical behaviour. As a Data Scientist, you'll join a growing, highly skilled team spanning data science, data engineering, AI engineering and DevOps. You'll play a key role in building and deploying machine learning and AI solutions that power business applications … Models, with the chance to integrate innovative AI capabilities including LLMs, MCPs, APIs and enterprise data into production platforms. You'll work closely with Engineering and Product teams in a collaborative Agile environment, turning complex data and technology into solutions that deliver tangible business value. The role will include ...

Senior AI Software Engineer, you will play a key role in designing, developing, and implementing intelligent software solutions that drive innovation, improve engineering productivity, and deliver measurable business value. You will work closely with AI Architects, Data Engineers, Data Scientists, Product teams, and Engineering colleagues to build scalable … Generative AI, AI-assisted code generation, automated testing, code review acceleration, agentic workflows, and development process optimisation. This role involves providing technical leadership, engineering expertise, and tooling oversight across the software delivery lifecycle, helping teams apply AI responsibly to modernise platforms, improve code quality, streamline delivery, and enhance developer ...

customer experience transformation by applying modern AI architecture in a secure, scalable, and responsible manner. This role collaborates closely with business leaders, product teams, engineering, and third-party partners to ensure data and AI investments deliver measurable value while meeting regulatory, operational, and security requirements.Main Duties/ResponsibilitiesStrategy & Architecture … senior leadership on AI strategy, feasibility and adoption paths.AI ArchitectureDefine architectural patterns and standards to support AI model lifecycle management, AI and data pipelines, feature engineering, and associated design artefacts.Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, knowledge ...

practices across the function Design and deploy machine learning models across forecasting, customer segmentation, and propensity modelling Own end-to-end model development including feature engineering, validation, deployment, and monitoring Partner with Engineering and Product teams to build scalable, production-ready data science solutions Translate analytical outputs ...

offer . Job Description About the role: Join us as a Software Engineer in our critical Scam Prevention team. This is a high-impact engineering role with a strong product focus, dedicated to solving complex, real-world adversarial problems to directly protect our global customer base. How we work … securing customers' money, accelerating product growth by raising automation levels, and helping us engineer a world-class money transfer product. What do you need? Engineering Expertise: Professional experience with Java or other JVM-based languages, including the Spring framework. Experience building and designing scalable web applications that handle large ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

research-only environment. You'll work on real business problems, build machine learning models that drive decision-making, and collaborate closely with engineering teams to see your work deployed and used at scale. What you'll be doing: Partnering with stakeholders to turn business challenges into Data Science solutions … looking for: Commercial experience as a Data Scientist Strong Python and SQL skills Hands-on experience building Machine Learning models Expertise in feature engineering, model evaluation and model selection Experience working with large datasets Exposure to Azure, AWS or GCP Strong stakeholder management and communication skills Experience taking ...

proficiency in deep learning frameworks such as PyTorch, with openness to learning new tools and technologies.Practical experience across ML and LLM software stack, including feature engineering, model development, deployment, and validation.Prior experience working with omics data and familiarity with oncology drug development.Excellent communication skills, with the ability ...

Data Science team of a large, established financial services business. The company has invested in its data platform and is building a dedicated ML Engineering capability to get more models into production. You'll work on real business problems, using large and varied datasets to develop models that … initial experimentation. You should be comfortable with: Python and common data science libraries. SQL and working with large datasets. Statistical modelling and machine learning. Feature engineering, model selection and evaluation. Writing clear, maintainable and testable code. Git-based development workflows. Working in a cloud environment such as Azure ...

data to trading infrastructure, where speed and reliability are fundamental requirements Building integrated environments where quantitative researchers develop, test, and deploy predictive models - from feature engineering through execution Architecting cloud-based simulation and experimentation platforms that support large-scale quantitative research without becoming a bottleneck Standing up toolchains … need to understand how data, models, and trading systems interact. You can work productively with quants and understand what they need from the engineering side. You are senior enough to take ownership. CTO wants people he can hand large initiatives to and trust the output. 6 to 15+ years ...