1 to 25 of 53 Feature Engineering Jobs in England

managers Design and deliver machine learning models at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Work with MLE Team and Stakeholders to help design and deliver data driven products using Trainline … and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have a broad of understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies. Have good communication skills Nice to have Understanding and/or hands-on experience of Reinforcement Learning theories, frameworks, and algorithms More ❯

vital component of the business, and is responsible for: Responsibilities Develop, train, and deploy machine learning models for risk scoring, behavioural analytics, fraud detection and extreme event detection. Optimize feature engineering, model performance, and real-time inference pipelines for large-scale datasets. Work on supervised, unsupervised, and reinforcement learning models to enhance decision-making. Leverage telematics, mobility, and … and Data Modelling Conduct exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities. Ensure robustness and scalability of data science pipelines, minimizing bias and improving accuracy. Data Engineering and Infrastructure Collaborate with the rest of the Engineering team to integrate machine learning models into production-grade systems. Work with big data processing frameworks (Spark, AWS, Azure … to scale data pipelines. Ensure efficient data wrangling, transformation, and feature selection using Python, SQL, and distributed computing. Optimize data workflows and cloud-based machine learning architectures, ensuring efficiency and performance. Collaboration and Cross-Functional Partnerships Work closely with Product, Engineering, and Commercial teams to align data science initiatives with business goals. Collaborate with Software Engineers to deploy More ❯

multiple internal and external sources. Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies. Design and implement data pipelines for model-ready datasets in collaboration with data engineering teams. Apply feature engineering and selection techniques to improve model accuracy and interpretability. Develop and validate machine learning and statistical models for prediction, classification, clustering, or optimization. … on project requirements. Evaluate models using appropriate metrics and perform hyperparameter tuning for optimal performance. Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams. Required: Translate model outcomes into actionable insights through clear storytelling and visualizations. Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools. Communicate findings to More ❯

to optimise operations, enhance exploration and production efficiency, drive the energy transition and improve decision-making across the organisation. The successful candidate will have a strong foundation in ML engineering principles and demonstrated prior experience working within the energy, oil, and gas, or a related industrial sector . Key Responsibilities Design, develop, and deploy robust, scalable, and production-ready … problems and translate them into actionable ML solutions. Build and maintain the necessary infrastructure for model training, versioning, deployment, and monitoring (MLOps). Conduct rigorous data exploration, cleaning, and feature engineering on large, complex, and often sparse energy-related datasets. Evaluate and optimize model performance, ensuring high accuracy, reliability, and interpretability in a high-stakes operational environment. Stay … MLOps to continuously improve AI capabilities. Ensure compliance with data privacy, security, and operational safety standards. Essential Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field. Minimum 3+ years of professional experience as an ML Engineer, Data Scientist, or in a similar role. Demonstrable and significant prior experience (2+ years More ❯

Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps … learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent analytical, communication, and organisational skills. Desirable - MS/PhD in More ❯

Responsibilities - Lead end-to-end execution of complex data science projects integrating statistical modelling, machine learning (ML), and deep learning (DL). - Collaborate closely with cross-functional teams (product, engineering, business stakeholders) to deliver data-driven solutions. - Design and conduct hypothesis testing (A/B and multivariate testing) and apply causal inference methodologies. - Perform exploratory data analysis, feature engineering, and develop models across traditional statistical and deep learning architectures. - Establish modelling frameworks with statistical quality control, detecting drift and decay. - Drive best practices for model explainability (e.g., SHAP/LIME) and scalable ML systems (including generative AI, NLP, CV, and recommendation engines). - Partner with engineering teams to ensure robust deployment and adherence to MLOps … learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent analytical, communication, and organisational skills. Desirable - MS/PhD in More ❯

PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open … source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression rules, integration objects, and data modelling. Skilled in building end … scraping, and Legacy system integration. Ability to assess when RPA vs. API integration vs. AI solutions are most appropriate, and experience building hybrid automation solutions combining multiple technologies. Data Engineering & Pipeline Development - Strong skills in building data pipelines for AI/automation solutions including data extraction, transformation, and loading (ETL). Experience with SQL databases (SQL Server), data validation More ❯

PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open … source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including process modelling, interface design, expression rules, integration objects, and data modelling. Skilled in building end … scraping, and legacy system integration. Ability to assess when RPA vs. API integration vs. AI solutions are most appropriate, and experience building hybrid automation solutions combining multiple technologies. Data Engineering & Pipeline Development - Strong skills in building data pipelines for AI/automation solutions including data extraction, transformation, and loading (ETL). Experience with SQL databases (SQL Server), data validation 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 ❯

powered tools (e.g. using GPT-class models) that assist with data extraction, document understanding, investor reporting, and internal decision-support Implementing end-to-end AI pipelines: data ingestion, cleaning, feature engineering, experimentation, model training, evaluation, and deployment Developing robust evaluation and monitoring frameworks for models (including regression tests, performance tracking, and drift detection) Working with structured and unstructured … founders, fast progression in a high-growth AI-driven fintech 🧠 Deep Technical Work: Opportunity to work on challenging, high-impact AI problems with real financial data and users 🛠 Modern Engineering Practices: Exposure to modern MLOps, experimentation workflows, and best practices in production AI 🤝 Collaborative Culture: Join a supportive, intellectually rigorous team that values deep thinking, ownership, and high-quality … engineering ✨ Additional Perks: Pension scheme, private healthcare, regular team socials, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your AI Master’s training to real-world problems in financial technology, please send us your CV. A member of the team will be in touch to discuss next steps. More ❯

powered tools (e.g. using GPT-class models) that assist with data extraction, document understanding, investor reporting, and internal decision-support Implementing end-to-end AI pipelines: data ingestion, cleaning, feature engineering, experimentation, model training, evaluation, and deployment Developing robust evaluation and monitoring frameworks for models (including regression tests, performance tracking, and drift detection) Working with structured and unstructured … founders, fast progression in a high-growth AI-driven fintech 🧠 Deep Technical Work: Opportunity to work on challenging, high-impact AI problems with real financial data and users 🛠 Modern Engineering Practices: Exposure to modern MLOps, experimentation workflows, and best practices in production AI 🤝 Collaborative Culture: Join a supportive, intellectually rigorous team that values deep thinking, ownership, and high-quality … engineering ✨ Additional Perks: Pension scheme, private healthcare, regular team socials, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your AI Master’s training to real-world problems in financial technology, please send us your CV. A member of the team will be in touch to discuss next steps. More ❯

machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments (AWS, Azure, or GCP) Collaborating with engineers, product teams, and … advancements in AI and machine learning What We’re Looking For: A recently completed Master’s degree from a Russell Group university in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow … remote work 📈 Career Growth: Mentorship, structured training, and clear progression paths 🛠 Modern Tech Stack: Hands-on with the latest AI and cloud tools 🤝 Collaborative Culture: Join a supportive, innovative engineering team ✨ Additional Perks: Pension scheme, private healthcare, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your academic and project work to real More ❯

machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments (AWS, Azure, or GCP) Collaborating with engineers, product teams, and … advancements in AI and machine learning What We’re Looking For: A recently completed Master’s degree from a Russell Group university in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow … remote work 📈 Career Growth: Mentorship, structured training, and clear progression paths 🛠 Modern Tech Stack: Hands-on with the latest AI and cloud tools 🤝 Collaborative Culture: Join a supportive, innovative engineering team ✨ Additional Perks: Pension scheme, private healthcare, and wellbeing initiatives How to Apply: If you’re excited by the opportunity to apply your academic and project work to real More ❯

lifecycle of AI solution development from concept to deployment and maintenance. Utilize programming skills in Python to automate processes and enhance functionality. Work with large datasets for data preprocessing, feature engineering, and model evaluation. Develop and maintain REST APIs and microservices architectures. Monitor, evaluate, and apply governance frameworks for AI/ML models in production environments. Prepare technical … documentation and effectively communicate complex concepts to both technical and non-technical stakeholders. Required Skills & Qualifications: Minimum 3 years of experience in AI solution engineering, specifically in enterprise environments. Demonstrable experience with Microsoft Azure AI services, including AI Foundry and Microsoft Copilot Studio. Proficiency in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. … Experience with Large Language Models, prompt engineering, and RAG implementations. Strong skills in data analytics, API development, and MLOps practices including CI/CD for ML. Excellent technical documentation and communication skills. Desirable knowledge in Docker, Kubernetes, and understanding of financial services or regulatory environments. In the first instance, please submit your CV. More ❯

workflows on AWS Building LLM applications (e.g. GPT, LLaMA, Claude) that automate policy checks, documentation, and audit trails Implementing end-to-end AI workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Developing scalable pipelines and APIs for AI services using AWS-native infrastructure (e.g. Lambda, ECS/EKS, S3, API Gateway, Step … Looking For: A recently completed Master’s degree from a top-tier university (e.g. Oxbridge, Imperial, UCL, or other leading Russell Group) in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, internships, or personal projects) applying machine learning or AI techniques Strong programming skills in Python and TypeScript More ❯

workflows on AWS Building LLM applications (e.g. GPT, LLaMA, Claude) that automate policy checks, documentation, and audit trails Implementing end-to-end AI workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Developing scalable pipelines and APIs for AI services using AWS-native infrastructure (e.g. Lambda, ECS/EKS, S3, API Gateway, Step … Looking For: A recently completed Master’s degree from a top-tier university (e.g. Oxbridge, Imperial, UCL, or other leading Russell Group) in Artificial Intelligence, Computer Science, Data Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, internships, or personal projects) applying machine learning or AI techniques Strong programming skills in Python and TypeScript More ❯

Responsibilities Model Development & Deployment: Design, build, and deploy scalable machine learning and statistical models for use cases such as pricing optimisation, customer behaviour prediction, and risk modelling. Data Exploration & Feature Engineering: Work with structured and unstructured data to uncover patterns and develop robust features for predictive models. Stakeholder Engagement: Collaborate with Technology, Product, Risk, and Marketing teams to More ❯

Responsibilities Model Development & Deployment: Design, build, and deploy scalable machine learning and statistical models for use cases such as pricing optimisation, customer behaviour prediction, and risk modelling. Data Exploration & Feature Engineering: Work with structured and unstructured data to uncover patterns and develop robust features for predictive models. Stakeholder Engagement: Collaborate with Technology, Product, Risk, and Marketing teams to More ❯

to our data science stack and capability, helping to foster a team culture of technical excellence and continuous improvement. You will also play a key role in our broader engineering culture, defining best practices, collaborating with other senior engineers, and fostering a mindset of continuous improvement. The Role As a Senior Data Scientist at Codat you will: Technical Leader … AI/ML into our core products, leveling up our offering and delivering novel value to clients. Alongside this, you will champion the use of AI tools to streamline engineering operations, enhance productivity, and upskill others across the team. AI-Driven Execution: Leverage AI to maximize productivity, be that in conducting research, gathering information or building solutions Be action … the right tools for complex problems and set technical standards for the team Advanced, hands-on expertise in SQL and big data platformslike Databricks, used for sophisticated data manipulation, feature engineering, and optimizing complex data workflows Extensive, proven experience in MLOps: owning the end-to-end lifecycle of production models , including designing scalable and reliable deployment strategies (e.g. More ❯

the following areas is a plus: finance, open banking, iGaming, startups, or enterprise companies dealing with real-time processing. Responsibilities You will use Python, with a strong grounding in feature engineering, model evaluation, and inference pipelines to help shape the future of our product offerings Lead data labeling at scale to produce ground-truth datasets and use ML … deployment and data workflows. Strong knowledge of statistical modelling, anomaly detection, clustering, and supervised/unsupervised learning Experience working with large-scale data Proven success collaborating with product and engineering teams to ship ML-based features and tools Strong communication skills and business acumen to present complex technical ideas to non-technical stakeholders Curious, proactive, and comfortable working in More ❯

the following areas is a plus: finance, open banking, iGaming, startups, or enterprise companies dealing with real-time processing. Responsibilities You will use Python, with a strong grounding in feature engineering, model evaluation, and inference pipelines to help shape the future of our product offerings Lead data labeling at scale to produce ground-truth datasets and use ML … deployment and data workflows. Strong knowledge of statistical modelling, anomaly detection, clustering, and supervised/unsupervised learning Experience working with large-scale data Proven success collaborating with product and engineering teams to ship ML-based features and tools Strong communication skills and business acumen to present complex technical ideas to non-technical stakeholders Curious, proactive, and comfortable working in More ❯

Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring, and iteration. • Tooling & Workflow Analysis: Review the ecosystem of analytical tools, frameworks, and environments used for data science, including reproducibility and collaboration readiness. More ❯

Framework Design: Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled. • Model Lifecycle Review: Assess practices across data preparation, feature engineering, model development, validation, monitoring, and iteration. • Tooling & Workflow Analysis: Review the ecosystem of analytical tools, frameworks, and environments used for data science, including reproducibility and collaboration readiness. 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 ❯

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 ❯