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

and methodologies to continually improve solution offerings. Provide expertise and guidance on AI best practices, contributing to the organization's AI strategy and innovation efforts. Conduct data analysis and feature engineering to prepare data for use in AI models, utilizing Azure Data Lake Develop robust testing and validation processes to ensure the accuracy and reliability of AI models … AI solutions, including Azure OpenAI Service, Azure Cognitive Services, and Azure Machine Learning. Familiarity with Azure Databricks is a plus. Solid background in machine learning algorithms, data pre-processing, feature engineering, and model evaluation. Experience with deep learning frameworks like TensorFlow or PyTorch is desirable. Proficiency in handling large datasets, experience with Azure Data Factory, Azure SQL Database … Azure networking and security services tailored for AI applications. Excellent communication and teamwork skills, with experience working in agile development environments. Qualifications University degree in computer science or software engineering and/or 5+ years equivalent work experience within a cloud environment. Cloud Certifications desirable Qualifications such as the following desirable: Microsoft Azure AI Engineer Fundamentals/Associate Microsoft More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn More ❯

Responsibilities Lead the design, development, and maintenance of credit risk and affordability models using bureau, open banking, and behavioural data Own the full model lifecycle from data sourcing and feature engineering to validation, deployment, and monitoring Design and run A/B and champion/challenger tests to improve performance across approval rates, losses, and customer experience Analyse … credit performance data to deliver insights that guide strategic decisions Mentor and support junior analysts/data scientists as the team expands Collaborate with data engineering to deploy models into production Work closely with stakeholders to define goals, communicate findings, and translate model outputs into business value About You You are an analytical thinker with a passion for using … data to drive credit decisioning. You bring hands-on experience building machine learning models for consumer credit and understand the nuances of data preparation, feature selection, and model validation in high-stakes environments. 5–7 years of experience in a credit-related data science or decision science role Proficiency in Python and experience with libraries such as scikit-learn 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 ❯

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 More ❯

performance, and scalability of ML/DL models. Manage and maintain ML systems, libraries, and pipelines, ensuring robust deployment into production. Analyse diverse datasets to inform model architecture and feature engineering. Stay current with state-of-the-art ML/DL research and evaluate new technologies for practical application. Mentor and guide junior ML engineers, sharing best practices and More ❯

relevance. Build production-ready Python code and integrate models into user-facing applications. Monitor and refine model performance post-deployment, making improvements based on feedback. Collaborate with product and engineering teams to deliver solutions aligned with business goals. Role Requirements 2-4 years' experience in applied machine learning and generative AI, including work with large language models. Strong Python … core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. Solid understanding of machine learning algorithms and statistical modelling techniques. A degree in Computer Science, Statistics, Machine Learning, Engineering, Physics, or a related More ❯

well as: Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, or a related field (desirable). Strong understanding of data structures, algorithms, and software engineering principles. Familiarity with data preprocessing, feature engineering, and model evaluation techniques. Familiarity with AI, machine learning, or blockchain technologies. Excellent communication, and problem-solving skills. Understanding of 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 ❯

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 ❯

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 ❯

Key Responsibilities: Design, develop, and deploy AI/Machine learning models and solutions, including LLMs and GenAI. Fine-tune and evaluate open-source LLMs, applying techniques such as prompt engineering and model re-tuning. Work with a variety of structured and unstructured datasets, handling preprocessing, cleaning, and feature engineering. Develop pipelines for creating, preparing, and optimising data for … business needs. Document workflows, data pipelines, and model processes for knowledge transfer and reproducibility. Key Skills & Experience: 4-5 years' experience across AI/ML, data science, or data engineering, with recent hands-on work in GenAI. Proven experience fine-tuning and deploying open-source LLMs. Strong knowledge of AI/ML algorithms and techniques (supervised, unsupervised, reinforcement learning … . Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training and tuning. Experience with big data tools More ❯

Key Responsibilities: Design, develop, and deploy AI/Machine learning models and solutions, including LLMs and GenAI. Fine-tune and evaluate open-source LLMs, applying techniques such as prompt engineering and model re-tuning. Work with a variety of structured and unstructured datasets, handling preprocessing, cleaning, and feature engineering. Develop pipelines for creating, preparing, and optimising data for … business needs. Document workflows, data pipelines, and model processes for knowledge transfer and reproducibility. Key Skills & Experience: 4–5 years’ experience across AI/ML, data science, or data engineering, with recent hands-on work in GenAI. Proven experience fine-tuning and deploying open-source LLMs. Strong knowledge of AI/ML algorithms and techniques (supervised, unsupervised, reinforcement learning … . Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training and tuning. Experience with big data tools More ❯

Key Responsibilities: Design, develop, and deploy AI/Machine learning models and solutions, including LLMs and GenAI. Fine-tune and evaluate open-source LLMs, applying techniques such as prompt engineering and model re-tuning. Work with a variety of structured and unstructured datasets, handling preprocessing, cleaning, and feature engineering. Develop pipelines for creating, preparing, and optimising data for … business needs. Document workflows, data pipelines, and model processes for knowledge transfer and reproducibility. Key Skills & Experience: 4–5 years’ experience across AI/ML, data science, or data engineering, with recent hands-on work in GenAI. Proven experience fine-tuning and deploying open-source LLMs. Strong knowledge of AI/ML algorithms and techniques (supervised, unsupervised, reinforcement learning … . Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training and tuning. Experience with big data tools 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 ❯

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 ❯

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 ❯