1 to 25 of 70 Feature Engineering Jobs in the UK

and simultaneously, maintain production models ensuring operational excellence as well as long term strategy. The ideal candidate will have a strong background in machine learning, data science, and software engineering, with the ability to design, develop, and implement robust machine learning models and systems in production. Key Responsibilities Lead the development of machine learning algorithms and models for behavioural … modeling and cybersecurity attack detection. Collaborate with cross-functional teams to understand requirements and translate them into effective machine learning solutions. Conduct exploratory data analysis, feature engineering, model development and evaluation. Work with infrastructure & product engineers to productionize models and new ML based features. Actively monitor and improve production models through feature engineering, rules and ML 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 ❯

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 deliver NLP based machine learning systems 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 Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state … 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 Experience with LangGraph or LangChain Experience with transport industry and/or geographical information 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 ❯

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 ❯

Aimpoint Digital is a premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which focuses on delivering solutions via machine learning and statistical modelling. What you will do As a part of Aimpoint … Become a trusted advisor working with clients to design end-to-end analytical solutions Work independently to solve complex data science use-cases across various industries Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights Write code in SQL, Python, and Spark following software engineering best practices Collaborate with … impact for your clients and do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment. In particular you have these traits: Degree in Computer Science, Engineering, Mathematics, or equivalent experience. Experience with building high quality Data Science models to solve a client's business problems Experience with managing stakeholders and collaborating with customers Strong written More ❯

and visibility functionality. As a senior member of the team, you'll be self motivated and be able to take ownership of projects, collaborate closely with stakeholders across product, engineering, and business to deliver data science solutions. Responsibilities Work across and support a range of data use cases including analytics and data provisioning Lead the design, development, and deployment … models and advanced analytics solutions to support recommendation and insight to production use cases Apply statistical techniques to extract insights and support data-driven decision-making Work alongside data engineering in requirements for data pipelines and feature engineering Promote best practices in data science, model validation, documentation, and reproducibility Qualifications Experience Strong coding skills in Python, including More ❯

ll Bring: We are seeking a seasoned professional who is excited by the unique challenges of AI data. Qualifications What are we looking for? Must-Have Skills: Extensive Data Engineering Experience: Proven track record (3+ years) in designing, building, and maintaining large-scale data pipelines and data warehousing solutions. Cloud Platform Mastery: Expert-level proficiency with at least one … data technologies like Apache Spark, Kafka, and data orchestration tools such as Apache Airflow or Prefect. ML Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage and retrieval. Generative AI Familiarity: Understanding … TensorFlow for data preparation in an ML context. Experience with real-time data streaming architectures. Familiarity with containerization (Docker, Kubernetes). Master's or Ph.D. in Computer Science, Data Engineering, or a related quantitative field. Additional Information Starcom has fantastic benefits on offer to all of our employees. In addition to the classics,Pension,Life Assurance, Private Medical and 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 ❯

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 ❯

/CD pipelines and automated deployment practices. Familiarity with DevOps concepts, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud infrastructure management (AWS, Azure, GCP). Experience in data engineering within Big Data ecosystems, including data pipelines and data integration. Solid understanding of software architecture and system design for high-availability applications. Knowledge of fundamental computer science concepts, including … maintain high-quality software development processes. Stay updated on the latest technologies and apply innovative solutions where applicable. Nice-to-Have Responsibilities (ML Focus): Develop, refine, and utilize ML engineering platforms and components as needed. Establish and manage processes for data preparation, feature engineering, and prediction. Closely monitor model performance and address any issues that arise. Explore More ❯

platforms (GCP preferred, AWS and Azure acceptable) Familiarity with Python ML packages such as PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas Strong SQL skills for data preparation and feature engineering Knowledge of MLOps principles, including automated retraining, monitoring, and deployment strategies Basic understanding of containerization and tools like Docker Nice-to-have skills: Experience with Google Cloud More ❯

Experience with common Python packages for Machine Learning - examples include PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas, etc. * Strong knowledge of SQL and its use for data preparation & feature engineering * Understanding of & practical experience with implementing MLOps principals - including automated model retraining, monitoring & deployment strategies * Some knowledge of containerisation & use of tools like Docker & Docker Compose Nice More ❯

machine learning and deep learning models. Contribute to scalable and reusable data pipelines using modern ML workflows. Conduct experiments and benchmarking exercises to test model performance. Perform error analysis, feature importance, and other model diagnostics. Track and log training/testing outcomes to support reproducibility and model versioning. Engineering Contributions Help build and integrate AI-powered APIs, scripts … tools, and containerization (e.g., Docker) to maintain codebase quality. Applied AI Domains Work on projects involving Natural Language Processing (NLP), Computer Vision, Generative AI, or Recommendation Systems. Support annotation, feature engineering, and augmentation tasks where necessary. Write clear, well-organized documentation for code, models, datasets, and workflows. Participate in team meetings, sprint planning, and code reviews. Engage with … to reflect on progress, set learning goals, and track outcomes. Required Qualifications A Bachelor's or Master's degree (completed or ongoing) in Computer Science, Data Science, Mathematics, Software Engineering, or a related STEM field. Eligibility to enroll in a Level 6 or Level 7 AI/ML/Data Science apprenticeship programme. Core Skills & Competencies Technical Skills Programming More ❯

Domain: Automotive Job Type: Permanent/Contract Location: Onsite in West Midlands, UK/Remote The ideal candidate brings hands-on experience in machine learning model development, integration with engineering simulations, and practical application to propulsion system delivery. You will be responsible for creating advanced digital tools that accelerate and enhance the performance, accuracy, and efficiency of propulsion system … Python, including ML libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, etc. Expert Level in programming such as VBA, Python, JAVA and other coding tools. Familiarity with data processing, feature engineering, and model evaluation techniques. Soft Skills: Strong analytical and creative thinking skills to apply AI in complex engineering contexts. Excellent communication skills for cross-functional collaboration … and presenting technical results. Initiative-driven with a passion for innovation and digital transformation. Education: Bachelor's or Master's degree in Mechanical Engineering, Computer Science, Data Science, Mechatronics, or a related field. Additional certifications or training in AI/ML or Data Engineering are highly desirable What we offer (For Permanent) Competitive Salary in line with level More ❯