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

monitoring processes Build a framework that supports the creation, deployment, maintenance, and monitoring elements that non-data scientist and machine learning analysts produce, including assisting with hyper-parameter tuning, feature engineering, feature selection, and validation, reporting and visualisation, and communication processes. Work closely with the data science team to integrate modelling approaches and techniques Key Skills and More ❯

monitoring processes Build a framework that supports the creation, deployment, maintenance, and monitoring elements that non-data scientist and machine learning analysts produce, including assisting with hyper-parameter tuning, feature engineering, feature selection, and validation, reporting and visualisation, and communication processes. Work closely with the data science team to integrate modelling approaches and techniques Key Skills and More ❯

Purpose of Position We are looking for an Data Engineering Manager to lead our AI/ML and Data Science Team. As a Data Engineering Manager, you will lead and mentor a team of 6-12 Data Engineers, fostering collaboration, ownership, and continuous learning to drive the success of our Affiliate Marketing platform. Your role involves overseeing data … solution delivery, aligning engineering efforts with business objectives, and ensuring best practices across data engineering processes. Beyond day-to-day management, you will play a key role in recruitment, career development, and strategic growth while facilitating collaboration between squads, departments, and stakeholders. Key tasks: Lead, mentor, and manage a team of 6-12 Data Engineers, ensuring their growth … Data Engineers. Establish clear team ownership and promote a culture of autonomy, knowledge sharing, and continuous improvement. Oversee the delivery of data solutions, ensuring alignment with business goals and engineering best practices. Run performance and development review processes, setting and tracking individual development goals through regular 1:1s. Provide constructive feedback, resolve conflicts, and create a supportive team environment. 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. Nice-to-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 and client More ❯

backend engineers to build and manage infrastructure and services for training and deployment of a diverse set of ML and NLP models - Build and maintain batch and real-time feature computation pipelines capable of processing complex structured and unstructured data using technologies such as Spark, Apache Airflow, AWS SageMaker etc. - Contribute to the implementation of foundational ML infrastructure such … as feature storage and engineering, asynchronous (batch) inference and evaluation - Apply your keen product mindset and tech savvy to help shape the future of our ML Platform, contributing to our progressive vision - Cultivate a supportive environment by providing thoughtful, actionable feedback, fostering growth and development among team members. What you bring to the team - Several years of industry … Azure/GCP) - Strong Python knowledge; experience developing and deploying production-grade software using asyncio - Hands-on experience with at least one Infrastructure-As-Code framework - Strong understanding of engineering and infrastructure best practices and general software development principles - Excellent communication abilities, ability to engage both technical and business audiences alike, and experience leading cross-functional projects Good to More ❯

have an exciting opportunity for a Data Scientist to join our rapidly growing Sports Forecasting & Analytics company. Purpose of the role As a Data Scientist within the Modelling & Data Engineering department, you will play a key role in the design, development, and implementation of predictive models that power our sports forecasting products. You will work collaboratively within a sport … from large-scale sports datasets using sound mathematical and statistical principles. Translate modelling requirements and business objectives into effective data science solutions, working closely with the Delivery Manager and Engineering teammates (Software and Data Engineers) within your modelling team. Perform data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation to support continuous model improvement. Write … able to contribute to cross-functional discussions. Proven problem-solving and time management skills. Desirable: Familiarity with cloud services (e.g. AWS S3, Athena, Lambda). Basic understanding of data engineering or ML pipelines. Knowledge of distributed systems (e.g. Kafka). Interest in American sports or the betting industry. Some exposure to .NET-based systems or application integration. Follow our More ❯

have an exciting opportunity for a Data Scientist to join our rapidly growing Sports Forecasting & Analytics company. Purpose of the role As a Data Scientist within the Modelling & Data Engineering department, you will play a key role in the design, development, and implementation of predictive models that power our sports forecasting products. You will work collaboratively within a sport … from large-scale sports datasets using sound mathematical and statistical principles. Translate modelling requirements and business objectives into effective data science solutions, working closely with the Delivery Manager and Engineering teammates (Software and Data Engineers) within your modelling team. Perform data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation to support continuous model improvement. Write … able to contribute to cross-functional discussions. Proven problem-solving and time management skills. Desirable: Familiarity with cloud services (e.g. AWS S3, Athena, Lambda). Basic understanding of data engineering or ML pipelines. Knowledge of distributed systems (e.g. Kafka). Interest in American sports or the betting industry. Some exposure to .NET-based systems or application integration. Follow our More ❯

have an exciting opportunity for a Data Scientist to join our rapidly growing Sports Forecasting & Analytics company. Purpose of the role As a Data Scientist within the Modelling & Data Engineering department, you will play a key role in the design, development, and implementation of predictive models that power our sports forecasting products. You will work collaboratively within a sport … from large-scale sports datasets using sound mathematical and statistical principles. Translate modelling requirements and business objectives into effective data science solutions, working closely with the Delivery Manager and Engineering teammates (Software and Data Engineers) within your modelling team. Perform data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation to support continuous model improvement. Write … able to contribute to cross-functional discussions. Proven problem-solving and time management skills. Desirable: Familiarity with cloud services (e.g. AWS S3, Athena, Lambda). Basic understanding of data engineering or ML pipelines. Knowledge of distributed systems (e.g. Kafka). Interest in American sports or the betting industry. Some exposure to .NET-based systems or application integration. Follow our 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 ❯

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 ❯

real-world systems, contributing to projects that have immediate business impact. Key Responsibilities Collaborate with senior leadership team to build ML/AI models. Perform data wrangling, cleaning, and feature engineering on varied datasets. Help deploy machine learning pipelines and support integration into production. Evaluate model performance and suggest improvements. Keep up with advancements in AI/ML More ❯

real-world systems, contributing to projects that have immediate business impact. Key Responsibilities Collaborate with senior leadership team to build ML/AI models. Perform data wrangling, cleaning, and feature engineering on varied datasets. Help deploy machine learning pipelines and support integration into production. Evaluate model performance and suggest improvements. Keep up with advancements in AI/ML More ❯

in this journey, working on cutting-edge projects that enhance our digital presence and improve customer engagement. As a Principal AI Engineer, you will be part of the Technology Engineering team within CIO, leading the design, development, and deployment of advanced AI and machine learning solutions that drive innovation, efficiency, and strategic value across the organisation. You will collaborate … closely with cross-functional teams, including Data Science, Engineering, Product, and Business stakeholders, to understand complex business challenges and identify opportunities where AI can deliver measurable impact. You will lead the end-to-end lifecycle of AI initiatives - from problem framing and data exploration to model development, deployment, and monitoring - ensuring solutions are scalable, ethical, and aligned with enterprise … goals. Skills we would love you to have: Extensive experience in AI/ML engineering, data science, or applied machine learning roles. Proven track record of delivering production-grade AI solutions in complex, data-rich environments. Proficiency in Python and key ML libraries (e.g. Semantic Kernel, Langchain and Agentic frameworks). Experience deploying models into production using APIs, containers 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 Additional Information What’s in 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 ❯

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 ❯