1 to 25 of 30 Feature Engineering Jobs in the UK excluding London

improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning workflows and help embed models into production … heavily imbalanced datasets Excellent skills in Python and SQL Solid understanding of classification algorithms such as gradient boosting decision trees, including pros and cons of different model architectures Strong feature engineering skills and experience in transforming raw data into useful model inputs Effective communication skills and able to explain complex findings clearly to both technical and non-technical More ❯

improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning workflows and help embed models into production … heavily imbalanced datasets Excellent skills in Python and SQL Solid understanding of classification algorithms such as gradient boosting decision trees, including pros and cons of different model architectures Strong feature engineering skills and experience in transforming raw data into useful model inputs Effective communication skills and able to explain complex findings clearly to both technical and non-technical More ❯

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 ❯

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 ❯

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 ❯

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 ❯

predictive models, and solutions using a range of model types-from small statistical approaches to large language models (LLMs)-that streamline processes and improve decision-making. You will apply engineering best practice to move rapidly from concept to production, ensuring every solution is ethical, fair, explainable, and compliant. Thriving in a collaborative environment, you will work closely with stakeholders … languages such as Java, JavaScript/TypeScript, or C++ is beneficial, but your core expertise is in the Python ecosystem Skilled in core Data Science practices including data preprocessing, feature engineering, model evaluation, data orchestration, and data structures, etc Hands-on with AIOps/MLOps/ModelOps tooling (Docker, Kubernetes, MLflow, model monitoring, CI/CD) Experience integrating More ❯

comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model More ❯

comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms (e.g., Regression, Classification, Clustering, Decision Trees, SVMs, Neural Networks) Model 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 ❯

and gain exposure to a wide range of tools, including cloud-based AI/ML platforms. What you'll do: Support development and optimisation of ML models through EDA, feature engineering, and testing Assist with data labelling, wrangling, validation, and governance Collaborate with stakeholders to deliver analytical insights and AI-driven solutions Use Python, SQL, and cloud-based 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 ❯

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 ❯

be doing: Designing and maintaining scalable data pipelines and infrastructure to support structured, analysis-ready data Designing and deploying end-to-end machine learning models from data preparation and feature engineering to model training, evaluation, and monitoring Overseeing a team of three data analysts and collaborating closely with business analysis and developers across William Reed Setting out a More ❯

be doing: Designing and maintaining scalable data pipelines and infrastructure to support structured, analysis-ready data Designing and deploying end-to-end machine learning models from data preparation and feature engineering to model training, evaluation, and monitoring Overseeing a team of three data analysts and collaborating closely with business analysis and developers across William Reed Setting out a More ❯

datasets (e.g. genomic sequences, patient records, imaging) Collaborate with bioinformaticians and clinical researchers to translate data into actionable insights Contribute to the development of internal tools for data preprocessing , feature engineering , and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries More ❯

datasets (e.g. genomic sequences, patient records, imaging) Collaborate with bioinformaticians and clinical researchers to translate data into actionable insights Contribute to the development of internal tools for data preprocessing , feature engineering , and model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries 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 ❯

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 ❯

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 ❯