20 of 20 Permanent Feature Engineering Jobs in the UK excluding London

Machine Learning, Artificial Intelligence, Predictive Analytics, Statistical Modelling, Python, SQL, PySpark, Azure, Databricks, MLflow, GitHub Actions, CI/CD, Time Series Forecasting, Propensity Modelling, Feature Engineering, Data Engineering, Azure Machine Learning, Azure Data Platform, Experiment Design, A/B Testing, Hypothesis Testing, Predictive Modelling, Customer Analytics, Marketing … that improve customer outcomes, enhance commercial performance & support strategic business decision making. Own the complete analytical lifecycle, from initial problem definition & exploratory analysis through feature engineering, model development, deployment, operational monitoring & continuous optimisation. Build sophisticated statistical & machine learning models covering areas such as: * Customer Lifetime Value * Customer Propensity ...

community. Please do not contact the recruiter directly. Your Role This role focuses on applying data science, statistics, machine learning, graph analytics, and KPI engineering to enable autonomous network intelligence. The AI Data Scientist will work with network telemetry, alarms, performance counters, inventory data, topology data, trouble tickets, service … network operations. Key Responsibilities Analyse large-scale telecom network datasets across RAN, Core, IP, Transport, SD-WAN, Cloud, OSS, and service domains. Develop KPI engineering models for network performance, service quality, fault behaviour, customer impact, capacity, and resilience. Build statistical and machine learning models for anomaly detection, fault prediction ...

Trust. Using Databricks as the primary development environment, you will build reproducible, auditable analytical workflows in SQL and Python, including data preparation, quality checks, feature engineering and modelling. You will also design and deliver reporting products and dashboards (for example in Power BI) to communicate clear, actionable insight … analytical and predictive models on curated Trust datasets. This includes full responsibility for data preparation activities such as data quality checks, cleansing, preprocessing and feature engineering, ensuring all work follows agreed definitions and is reproducible and auditable. The postholder will create reporting products and dashboards (for example using ...

powered products and customer-facing features • Apply machine learning techniques to solve complex business problems • Support experimentation through modelling, statistical testing and evaluation • Conduct feature engineering and feature analysis to improve model performance • Work closely with Product, Engineering and Analytics teams on high-impact initiatives • Build ...

Work With Building and deploying AI and Machine Learning solutions Designing and implementing end-to-end AI and ML pipelines, from data acquisition and feature engineering through to deployment and optimisation Developing scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native … stakeholders to understand business challenges and translate requirements into AI solutions Working closely with technical and non-technical teams throughout programme delivery Cross-functional engineering, architecture and data teams Agile software development and delivery methodologies Exploring and adopting emerging AI technologies and approaches to drive innovation and business value ...

NumPy, scikit-learn and relevant statistical or machine learning packages Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking ...

NumPy, scikit-learn and relevant statistical or machine learning packages Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking ...

concept into production environments Develop scalable ML pipelines using AWS cloud technologies Implement MLOps and LLMOps best practices for deployment, monitoring and governance Support feature engineering, model optimisation and performance monitoring Apply responsible AI principles, including model explainability and fairness Present findings and recommendations to technical … tools such as MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication ...

security platform, contributing to innovative features that leverage advanced analytics, behavioural detection, and intelligent threat prevention. Working as part of a highly collaborative engineering team, you will help bridge the gap between cutting-edge machine learning research and production-grade security software. To see what our engineering teams … design and implementation of intelligent threat detection, behavioural analysis, and anomaly detection capabilities. Participate in architecture discussions, code reviews, testing, and continuous improvement of engineering practices. Evaluate emerging AI technologies and contribute to future product innovation. Requirements for the C++ AI/ML Software Engineer Strong proficiency in modern ...

play a key role in designing, developing and deploying AI and Machine Learning solutions, working across the full ML lifecycle from data preparation and feature engineering through to model deployment and optimisation. Working with Python, SQL and leading ML frameworks, you'll build scalable pipelines, develop and evaluate ...

Senior Data Scientist to take a leading role across a portfolio of technically demanding projects. You’ll operate end-to-end - shaping problem definitions, engineering data pipelines, developing advanced models, and ensuring outputs are robust, explainable and deployable in real-world settings. This is a hands-on role … impact. Key Responsibilities Translating complex, ambiguous problem statements into clear, actionable data science solutions. Owning the full data science lifecycle, from data ingestion and feature engineering through to modelling, evaluation and deployment. Developing statistical, machine learning and deep learning models to support high-impact, real-world decision making. ...

here at Sky. Role/Team overview We are seeking an AI Specialist to design, build, and operationalise intelligent systems that enhance Site Reliability Engineering (SRE) platforms and automate large-scale infrastructure operations. This role has a strong emphasis on reliability, scalability, and operational efficiency. You will work closely … with SRE, platform, and cloud engineering teams, within a global technology support organisation to embed AI-driven decision-making, predictive analytics, and autonomous remediation into infrastructure platforms. Whatyou’lldo Infrastructure Automation at Scal e Design and deploy AI-powered automation frameworks for incident response and remediation (self-healing systems ...

here at Sky. Role/Team overview We are seeking an AI Specialist to design, build, and operationalise intelligent systems that enhance Site Reliability Engineering (SRE) platforms and automate large-scale infrastructure operations. This role has a strong emphasis on reliability, scalability, and operational efficiency. You will work closely … with SRE, platform, and cloud engineering teams , within a global technology support organisation to embed AI-driven decision-making, predictive analytics, and autonomous remediation into infrastructure platforms . What youll do Infrastructure Automation at Scal e Design and deploy AI-powered automation frameworks for incident response and remediation (self ...

optimization techniques for LLM-based agents Resource-constrained AI model deployment Vector databases and semantic search (e.g., Pinecone, Weaviate, Milvus, Qdrant , and ChromaDB ) Prompt engineering for efficient agent reasoning LLM fine-tuning and instruction optimization Graph databases for network topology and context management Feature engineering for network … deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com ...

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 … 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 ...

using Python, TensorFlow, PyTorch Work on supervised, unsupervised, and reinforcement learning problems Clean and process large datasets to extract insights and train algorithms Perform feature engineering to improve model accuracy Design ML pipelines and automate model deployment Collaborate with software engineers to integrate models into applications Use MLOps ...

then scale into production Shape problem definitions with stakeholders in ambiguous environments Bring best practices from adjacent industries into a high-performance setting Data Engineering & Architecture Design/maintain pipelines integrating telemetry/timing, video/image, event operations, and digital/commercial engagement data Develop APIs/services … secure-by-design delivery with cyber teams What we're looking for Essentials 5-10 years' experience in data science/AI/ML engineering Strong Python Production ML deployment experience (end-to-end) Strong SQL + modern data warehouse experience Cloud familiarity + containerisation/orchestration Streaming data ...

typically lead a team or the solution delivery effort, demonstrating technical excellence through leading by example. You could be providing technical support, leading an engineering team or working across multiple teams as a subject matter expert who is critical to the success of a large programme of work. Your … deployments. Shape team behaviour for writing specifications and acceptance criteria, estimating stories, sprint planning and documentation. Actively define and evolve PA’s data engineering standards and practices, ensuring we maintain a shared, modern and robust approach. Lead and influence technical discussions with client stakeholders to achieve the collective ...

real-world GenAI and ML solutions across the entire LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering-everything from predictive models to enterprise GenAI apps to automation that transforms how teams operate . We are data science generalists who love variety. … designing a new GenAI workflow, the next it is deploying a model into Salesforce or engineering a pipeline in Databricks. We own our projects end-to-end and partner directly with stakeholders to deliver solutions that get used and make a measurable difference. If you want to experiment, build ...

data to trading infrastructure, where speed and reliability are fundamental requirements Building integrated environments where quantitative researchers develop, test, and deploy predictive models - from feature engineering through execution Architecting cloud-based simulation and experimentation platforms that support large-scale quantitative research without becoming a bottleneck Standing up toolchains … need to understand how data, models, and trading systems interact. You can work productively with quants and understand what they need from the engineering side. You are senior enough to take ownership. CTO wants people he can hand large initiatives to and trust the output. 6 to 15+ years ...