51 to 70 of 70 Feature Engineering Jobs in the UK

science solutions that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to … and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) from a strong university Excellent communication skills and a collaborative mindset Please note: This role cannot offer VISA sponsorship More ❯

science solutions that deliver commercial value Lead the design, development, and deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to … and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and client stakeholders Bachelor’s or Master’s degree in a quantitative field (e.g., Mathematics, Physics, Engineering, Computer Science) from a strong university Excellent communication skills and a collaborative mindset Please note: This role cannot offer VISA sponsorship More ❯

production-level solutions Troubleshoot and debug code Work with other teams to understand and solve business problems About you: Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineering SQL: For querying structured data sources Model Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking models Machine Learning Deployment: Familiarity with containerised deployment More ❯

happy - by continuously training and deploying machine learning models. We aim to make model deployments as easy and error-free as code deployments. Google's Best Practices for ML Engineering is our bible. Our models are trained to spot multiple types of fraud, using a variety of data sources and techniques in real time. The prediction pipelines are under … it's down to the Detection team to investigate why. The Detection team is core to Ravelin's success. They work in a deeply collaborative partnership with the Data Engineering team to design the data architecture and infrastructure that powers our ML systems. The Role We are looking for a Senior Machine Learning Engineer to join our Detection team. … Beyond just consuming data, you will take a leading role in defining how data is modeled, stored, and served for machine learning purposes, directly influencing the architecture of our feature generation pipelines and ensuring data quality throughout the ML lifecycle. You'll take strategic ownership over several aspects of our ML infrastructure and be empowered to introduce and champion More ❯

star customer service. The team The Acquisition Tech team focuses on acquiring new customers through various channels and technologies. You'll be responsible for driving growth through data engineering and tooling. Your primary focus will work with the marketing and sales teams to attract new customers while also ensuring compliance with data-privacy regulations, including unsubscribes and vulnerable person … guidelines. The role The projects you'll work on will be diverse and will demand a strong proficiency in multiple technologies, primarily Python and SQL. Your tasks may include feature engineering for machine learning models or data validation, integrating with external APIs, developing internal tools, creating REST APIs, and managing databases. The requirements Essential: Python software engineering … dynamic, and autonomous environment. Bonus: Mastery of SQL, Alchemy, scikit-learn, pandas, and PostgreSQL. Experience designing, building, and managing relational databases. Strong numerate background, i.e. maths, physics, comp-sci, engineering, etc. DevOps exposure: i.e. containerization, continuous integration/deployment (CI/CD). The salary We expect to pay from £50,000 - £80,000 for this role. But, we More ❯

Troubleshoot, debug, and refine model code Partner with cross-functional teams to translate business needs into data-driven solutions Essential skills: Python (pandas, NumPy, scikit-learn) – data wrangling, modelling, feature engineering SQL – querying structured datasets Model Development & Validation – classification, unsupervised learning (outlier detection), ranking models ML Deployment – containerised deployments (Podman, SageMaker, DSW pipelines) Git – version control for reproducible More ❯

Troubleshoot, debug, and refine model code Partner with cross-functional teams to translate business needs into data-driven solutions Essential skills: Python (pandas, NumPy, scikit-learn) – data wrangling, modelling, feature engineering SQL – querying structured datasets Model Development & Validation – classification, unsupervised learning (outlier detection), ranking models ML Deployment – containerised deployments (Podman, SageMaker, DSW pipelines) Git – version control for reproducible More ❯

markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience … performance data to generate actionable insight and support strategic decisions Mentor and develop a small team of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science More ❯

markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience … performance data to generate actionable insight and support strategic decisions Mentor and develop a small team of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science More ❯

markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience … performance data to generate actionable insight and support strategic decisions Mentor and develop a small team of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science More ❯

markets. Responsibilities: Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data. Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring. Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience … performance data to generate actionable insight and support strategic decisions Mentor and develop a small team of analysts/data scientists as the team scales Work closely with Data Engineering to deploy models into production pipelines. Collaborate with stakeholders to define modelling goals and interpret model outcomes in a business context. Requirements: MSc or PhD Degree in Computer Science More ❯

medicine, artificial intelligence and more. Our office in Islington has state of the art cryogenic facilities and an outstanding interdisciplinary team spanning quantum physics to IC design. The Cloud Engineering Team builds cloud-based solutions, supports the cloud platform and helps to drive its adoption and expansion while also collaborating with other teams. Furthermore, the Cloud Engineering Team … RESTful APIs, web frameworks, NoSQL databases and serverless applications Strong understanding of version control systems and familiarity with CI/CD Pipelines Bachelor's degree in Computer Science, Software Engineering, or related fields Working knowledge of AWS services A logical approach to problem solving. Experience - Desirable Awareness of DevOps and Agile principles Experience with IaC and DevOps tooling such … Terraform, Packer, Ansible, Chef, Gitlab Experience with containerization technologies and orchestration tools Familiarity with data preprocessing and feature engineering techniques for ML model training. Familiarity with MLOps tooling Benefits Be part of a creative, world-leading team Competitive salary and share options scheme Contributory pension scheme Private Medical Insurance Life Assurance Cycle-to-work Scheme Flexible working Central More ❯

commercial uplift—delivering solutions that are accurate, explainable, and production-ready. Key Responsibilities Build machine learning models for forecasting, propensity scoring, and segmentation Own workflows from data wrangling and feature engineering through to deployment and monitoring Operationalise models using MLOps tools in a cloud-native environment Architect datasets by merging structured and semi-structured data Work closely with More ❯

commercial uplift—delivering solutions that are accurate, explainable, and production-ready. Key Responsibilities Build machine learning models for forecasting, propensity scoring, and segmentation Own workflows from data wrangling and feature engineering through to deployment and monitoring Operationalise models using MLOps tools in a cloud-native environment Architect datasets by merging structured and semi-structured data Work closely with More ❯

design, dev, test, deployment, configuration, documentation) to meet the business requirements. • The role is hybrid, and the expectation is that you attend the office according to Mastercard policy. • Own feature engineering within the team, collaborating with a separate data science to understand and implement their requirements. • Bridge the gap between architecture and engineering, work alongside product architects … You have experience optimising solution performance with a constrained set of technologies. • You have experience or are keen to engage with productionising machine learning technologies combined with large scale feature engineering. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every More ❯

and work with many different clients in many different sectors Your profile: 3+ years of prior work experience in a relevant field Experience in data importing, exporting, visualisation and featureengineering Experience of user centered design experience required.Available portfolio of in-market examples of successful user interface design, creative synthesis and storytelling skills, design thinking and agile approach to problem solving More ❯

a plus, but not a hard requirement. Preferable experience in alpha research/signal generation, or worked on systematic trading strategy development. Deep understanding of data science practices e.g. feature engineering and signal combining. Bachelor's degree or higher in a Quantitative discipline e.g. Maths, Computer Science, Engineering, Physics, or Statistics. Compensation Competitive base salary. Offers can More ❯

a unique opportunity to work on diverse challenges and contribute to cutting-edge solutions that drive significant impact for the business. You will gain expertise in ML infrastructure, data engineering, cloud computing, and scalable system design while working closely with our ML scientists to bring their models to life in production. This role requires an individual with a strong … foundation in software engineering and machine learning infrastructure, who is passionate about building robust and scalable systems. You should be comfortable with ambiguity, enjoy tackling complex technical challenges, and have a keen interest in optimizing ML workflows. We welcome candidates who are strong problem solvers and passionate about building ML systems . E ven if you don't meet … to hear from you. In this role, you will: Design and implement scalable infrastructure for deploying ML models in production. Build and maintain data pipelines for efficient processing and feature engineering. Optimize compute resources and enhance model serving performance. Set up monitoring and logging systems for deployed ML models. Collaborate with ML scientists to streamline development-to-production workflows. More ❯

success criteria. Support and mentor junior team members, fostering a collaborative environment. Independently manage client relationships and project expectations. Develop and deploy ML models, applying innovative approaches beyond basic feature engineering. Use Python, SQL, and Tableau for reporting and analysis within an AWS environment. Work closely with Data Engineering, Sales, and Market Research teams across the UK, US … meaningful insights to drive business decisions. Strong communication skills, with experience in managing client relationships. A degree in a relevant field such as Mathematics, Statistics, Economics, Psychology, Sociology, Physics, Engineering, or Computer Science (Master’s or PhD is a plus). Interest or experience in TV viewing data, media, or marketing analytics is beneficial but not essential. If this More ❯

the best tools into the tech stack. Prototyping, testing, and refining AI applications to improve accuracy and performance. Writing clean, modular Python code and performing rapid data processing and feature engineering. Identifying and prioritising opportunities for AI implementation based on business needs and feedback. Translating complex technical concepts into clear, accessible explanations for stakeholders. What we're looking for … Essential: Strong experience with Gen AI, NLP, and machine learning. Proven ability to create, train, and fine-tune LLMs/SLMs. Skills in prompt engineering and RAG (retrieval-augmented generation). Solid background in Python, Java, and database technologies (SQL/NoSQL). Experience in crawling/web scraping and API development. Experience with on-premise AI development (local More ❯