26 to 50 of 55 Feature Engineering Jobs in England

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 ❯

help develop advanced machine learning models that predict and prevent equipment failures in large-scale industrial operations. This is an exciting opportunity to apply data science to real-world engineering challenges , using rich, high-frequency data from complex machinery. Their AI-driven predictive maintenance platform is built on years of operational data and designed to help clients reduce downtime … maintenance strategies. Develop and maintain data pipelines using tools like Apache Airflow for efficient workflows. Use MLflow for experiment tracking, model versioning, and deployment management. Contribute to data cleaning, feature engineering, and model evaluation processes. Collaborate with engineers and data teams to better understand equipment behavior and enhance model performance. What We're Looking For A Bachelor's … degree in Computer Science, Mathematics, Electrical Engineering, or a related field. Strong experience with Python and data science libraries (Pandas, Scikit-learn, etc.). Solid understanding of machine learning concepts and algorithms . Interest in working with real-world industrial or sensor data . Exposure to Apache Airflow and/or MLflow (through coursework or experience) is a plus. More ❯

to support real-time health assessment. Collaborate with electronics engineers to interface machine learning models with handheld and wearable sensor systems Develop pipelines for real-time data acquisition and feature extraction and evaluate model performance and system-level integration Establish rigorous data governance and pre-processing protocols to ensure data integrity, security, and compliance with healthcare standards Work closely … research findings, supporting translation into practice through collaborations, providing training and helping to secure research funding About you: PhD or equivalent industrial experience in Computer Science, Data Science, Biomedical Engineering, Applied Mathematics, or a closely related discipline with a focus on machine learning or data-driven modelling Proven expertise in developing, training, and validating machine learning and statistical models … and real-time data interpretation Demonstrated ability to integrate ML algorithms with sensor systems, or embedded hardware Proficiency in Python, MATLAB, or equivalent programming environments Experience in data curation, feature engineering, and pre-processing for multimodal healthcare or sensor datasets Strong track record of publishing research in peer-reviewed journals or writing industrial reports. Excellent communication skills, including 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 Act as a technical lead within project teams, mentoring mid … 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 Act as a technical lead within project teams, mentoring mid … 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 ❯

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. A degree in Computer Science, Statistics, Machine Learning, Engineering, Physics, or a related quantitative field. More ❯

experience in a data science or machine learning role Strong programming skills in Python and familiarity with key data libraries (e.g. pandas, NumPy, scikit-learn) Experience with data wrangling , feature engineering, and model optimisation Understanding of data pipelines , APIs, and production deployment workflows Excellent communication skills and a collaborative approach Why Apply? Work on mission-critical UK Government More ❯

experience in a data science or machine learning role Strong programming skills in Python and familiarity with key data libraries (e.g. pandas, NumPy, scikit-learn) Experience with data wrangling , feature engineering, and model optimisation Understanding of data pipelines , APIs, and production deployment workflows Excellent communication skills and a collaborative approach Why Apply? Work on mission-critical UK Government More ❯

our Machine Learning Platform. Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise … break before wrapping up the day with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across Europe. No matter the More ❯

Scientist to lead the development and application of advanced analytics and machine learning techniques that answer complex business questions and drive strategic decisions. You'll work closely with data engineering and business teams to design models and analytical solutions that improve customer experience, optimise operations, and support financial and strategic planning.This role combines technical expertise with leadership responsibilities, including … Mentor team members, present insights clearly to technical and non-technical audiences, and ensure best practices in analytical workflows. Technical Delivery: Engineer and optimise analytical and spatial SQL, build feature enrichment pipelines, develop and evaluate ML models, and package outputs for deployment. What You'll Need to Succeed Strong experience in applied machine learning (regression, tree ensembles, experiment design … . Advanced proficiency in Python and SQL, with experience in spatial SQL and PostGIS. Familiarity with spatial tools and libraries (GeoPandas, QGIS) and feature engineering concepts. Experience with data modelling, dbt, and version control (Git). Knowledge of spatial datasets (MasterMap, AddressBase, Land Registry). Desired: Experience with WMS/WFS services, graph theory (NetworkX), GDAL, and Snowflake. More ❯

you'll be doing Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise … break before wrapping up the day with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across Europe. No matter the More ❯

Mastering and deploying robust Retrieval-Augmented Generation (RAG) pipelines, integrating LLMs with complex enterprise data sources for unparalleled accuracy. - END-TO-END DELIVERY: Owning the entire solution lifecycle, from feature engineering and data mapping to scalable, production-ready deployment. - CLOUD EXCELLENCE: Working closely with Data Engineers to utilize and optimize the full potential of the [Cloud Environment] ecosystem. … familiarity with the [Specific Vendor]’s [Cloud Environment] AI and Data Stack (Azure ML, Azure OpenAI, Databricks). - SPECIALIZATION: Expertise in Vector databases (FAISS, Pinecone, Azure AI Search), Prompt Engineering, and familiarity with products like [Specific Vendor Product 1]. THE CULTURE Our client seeks curious, pragmatic problem-solvers who embody their core values: Integrity, Drive, Empathy, Adaptability, and 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 What’s in it for More ❯

a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion … feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration: Collaborate with existing data capture and execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous … integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and latency optimisation. Experience integrating ML workflows with existing data and execution systems. Strong understanding More ❯

a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion … feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration: Collaborate with existing data capture and execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous … integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and latency optimisation. Experience integrating ML workflows with existing data and execution systems. Strong understanding More ❯

as; AI Foundry, Open-AI services, and Microsoft Copilot Studio. Apply governance frameworks for AI/ML models. Python knowledge to automate processes Large datasets experience, including data preprocessing, feature engineering, and model evaluation. Agile development of AI solution from concept to deployment to continuous improvement. Create and maintain technical documentation Communicate complex concepts to all stakeholders. At … least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯

is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities. This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale. The … of their real-time modelling infrastructure. This is a hybrid hands-on/leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science. What You’ll Be Doing Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases Spearhead the entire … s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring Define best practices for model testing, automation, and continuous improvement within a high-performing team Act as a technical thought leader, partnering with More ❯

is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities. This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale. The … of their real-time modelling infrastructure. This is a hybrid hands-on/leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science. What You’ll Be Doing Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases Spearhead the entire … s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring Define best practices for model testing, automation, and continuous improvement within a high-performing team Act as a technical thought leader, partnering with More ❯

the structure and purpose of their outputs and determine the most effective ways to visualise and communicate them. Data Product Development Support the development of robust data pipelines and feature engineering activities in partnership with Data Engineers. Design and develop a unified, user-friendly front-end interface that integrates dashboards, reports, models, and pipeline outputs into a single … or equivalent). Solid understanding of UX principles and accessibility standards (WCAG 2.2). Ability to collaborate with multidisciplinary teams and communicate complex information effectively. Desirable Experience with data engineering concepts such as pipelines, orchestration, or feature engineering. Familiarity with component-driven design systems. Knowledge of performance optimisation techniques for front-end applications. Experience working in Agile or More ❯

and simulation teams to integrate data science models into larger decision engines Collaborate with software engineers to productionise models and embed them in decision-support platforms Conduct data cleaning, feature engineering, and data pre-processing to ensure model quality and robustness Document model design, assumptions, and data sources Present findings, insights, and recommendations to stakeholders, including non-technical More ❯

and simulation teams to integrate data science models into larger decision engines Collaborate with software engineers to productionise models and embed them in decision-support platforms Conduct data cleaning, feature engineering, and data pre-processing to ensure model quality and robustness Document model design, assumptions, and data sources Present findings, insights, and recommendations to stakeholders, including non-technical More ❯

using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

an internal evangelist for applied data science and innovation 🧠 What You’ll Bring Must-haves Strong understanding of machine learning techniques — clustering, NLP, deep learning Hands-on experience with feature engineering, model evaluation and data exploration Knowledge of modern AI tooling (RAG, fine-tuning, LLMs, agentic frameworks) Ability to bridge technical and commercial conversations confidently A logical, creative More ❯