1 to 25 of 37 Permanent 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 ❯

clean, and preprocess structured and unstructured data from diverse sources Perform exploratory data analysis (EDA) to uncover trends and anomalies Design and implement data pipelines in collaboration with data engineering teams Apply feature engineering and selection techniques to enhance model performance Build and validate ML models for prediction, classification, clustering, and optimization Use libraries such as Scikit 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 ❯

teams to automate workflows, monitor performance, and retrain models as needed. You'll bring a passion for building reliable ML infrastructure, a strong technical foundation in modern machine learning engineering, and a track record of working in environments where reliability and scale are paramount. Our Data & Analytics team has grown significantly over the last 18 months, with strong ongoing … ML pipelines for training, deployment, monitoring, and retraining at scale. Working with data scientists to take models from development to production-grade systems, ensuring scalability, reproducibility, and robustness. Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using … Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to deliver ML features that directly impact customer experience. Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs). Monitoring deployed solutions in More ❯

engineering. Design and implement AI-powered solutions, leveraging Python, SQL/NoSQL, and APIs for seamless integration. Prototype and rigorously test AI applications, focusing on improving performance through strategic feature engineering and model optimization. Engage with the latest open-source AI/ML tools to enhance business solutions continuously. Develop, deploy, and manage machine learning models aimed at … of various ML algorithms including supervised, unsupervised, and deep learning. Strong problem-solving and effective communication skills. Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Engineering, or related field. Benefits: 25 days annual leave plus bank holidays Performance-based bonus scheme linked to individual, team, and company achievements Generous Defined Contribution Pension scheme Permanent Health 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 ❯

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 ❯

quantitative models and metrics, extend, and maintain quantitative models, metrics and investment frameworks, with rigorous back‐testing, scenario design, and attribution . Integrate new indicators and alternative datasets; formalise feature engineering and signal decay/robustness analysis; implement model risk controls , documentation, and reproducibility. Scale and commercialise proprietary metrics for investment use‐cases and new revenue lines (e.g. … in Physics/Mathematics/Computer Science/Quantitative Finance or related STEM field (exceptional MSc considered with relevant work experience). 4–7 years in quantitative research/engineering within capital markets; proven delivery of research to production quant solutions at scale. Demonstrable experience with large scale datasets, data engineering workflows, and production automation. Python (production grade … SQL, Linux; strong software engineering (testing, type hints, code review, packaging). Cloud (AWS/Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious 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 ❯

across the organization. Responsibilities Understand business problems and conduct statistical analysis independently. Break down hard problems. Communicate effectively to senior stakeholders. Deliver value end-to-end. Self-serve data engineering and infrastructure as required. Make recommendations on best practice in terms of analysis, machine learning and data science. Have a transformative presence in the team. Develop and implement machine … learning models, including feature engineering, model design, training, and deployment. Perform data mining, exploration, and statistical analysis to uncover trends and actionable insights. Create data visualizations, reports, dashboards, and perform data audits. Leverage predictive models to optimize customer experiences and drive business outcomes. Create automated anomaly detection systems to monitor and ensure data quality and operational performance. Qualifications … Computer Science, or a related field. Doctorate in a quantitative field such as Statistics, Computer Science, Mathematics, or Engineering. A "full stack" data scientist - with extensive expertise of data engineering, analysis and analytics, as well as machine learning. 6+ years of experience working in Data Science, preferably within a Software organization. Experience with financial fraud detection and prevention is 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 ❯

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 ❯

Key Skills Proficiency in Python and/or R for developing machine learning models. Experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms like AWS, GCP, or Azure for deploying ML solutions. Knowledge of version control systems (e.g., Git) and CI/ More ❯

and product managers, to deliver robust AI solutions. Key Skills Proficiency in Python, TensorFlow, or PyTorch Experience with machine learning algorithms and frameworks Strong understanding of data pre-processing, feature engineering, and model evaluation Familiarity with cloud services (AWS, GCP, Azure) and their AI/ML offerings Knowledge of natural language processing (NLP) and computer vision is a 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 ❯

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 ❯

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 ❯

impact. You’ll remain hands-on in design and experimentation while mentoring 1–2 junior ML engineers. You’ll collaborate closely with credit analysts, data scientists, and the wider engineering team to ensure our models are accurate, explainable, and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and … XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing and evaluation to enhance predictive performance and interpretability. Use BigQuery and GCP tools (including CloudRun) to manage and process large-scale datasets efficiently. Ensure models are explainable and … teams to interpret outcomes. Mentor junior ML engineers through code reviews, technical guidance, and project planning. Work with software engineers to productionise models (deployment and pipelines handled by the engineering team). Stay ahead of emerging ML techniques and bring new ideas to improve scalability, performance, and transparency. What We’re Looking For 4+ years of hands-on experience More ❯

impact. You’ll remain hands-on in design and experimentation while mentoring 1–2 junior ML engineers. You’ll collaborate closely with credit analysts, data scientists, and the wider engineering team to ensure our models are accurate, explainable, and seamlessly integrated into our lending platform. What You’ll Do Lead the design, training, and optimisation of credit risk and … XGBoost and scikit-learn. Responsible for creating proprietary data enrichment algorithms. Guide the evolution toward a self-learning model framework, improving automation and adaptability over time. Design and oversee feature testing and evaluation to enhance predictive performance and interpretability. Use BigQuery and GCP tools (including CloudRun) to manage and process large-scale datasets efficiently. Ensure models are explainable and … teams to interpret outcomes. Mentor junior ML engineers through code reviews, technical guidance, and project planning. Work with software engineers to productionise models (deployment and pipelines handled by the engineering team). Stay ahead of emerging ML techniques and bring new ideas to improve scalability, performance, and transparency. What We’re Looking For 4+ years of hands-on experience More ❯

will help shape our Data & Productivity capability—designing, training, and deploying models with MLflow/AutoML, and integrating them into real business processes. You'll collaborate across disciplines (Data Engineering, Security, Product) and work with services such as Azure Storage, Azure DevOps/GitHub Actions, and Fabric/Power BI to ensure solutions are observable, governed, and production-ready. … AI lifecycle. Experienced in designing, training, registering, and deploying models using Azure Machine Learning (AML), AutoML, and MLflow. Proficient in Python, pandas, and scikit-learn for data science and feature engineering, with a strong focus on rigorous validation and experiment tracking. Skilled in deploying models to real-time REST endpoints and orchestrating batch inference pipelines. Capable of implementing … MLOps practices including CI/CD pipelines, model registry, environment management, and promotion across dev/test/prod stages. Solid understanding of cloud and data engineering principles including storage, compute, and pipeline orchestration. Strong ability to collaborate with subject matter experts to map current-state business processes and translate them into machine learning opportunities with measurable operational impact. 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 ❯

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 ❯