23 of 23 Permanent Feature Engineering Jobs in England

models by converting analysts' Python code into scalable, maintainable and deployable workflows Design, build and maintain data pipelines for pricing inputs, including ingestion, transformation, feature preparation and quality checks Build and automate workflows for elasticity, forecasting, optimisation and other pricing models Develop and manage API integrations, including competitor pricing … feeds and downstream systems Work closely with pricing analysts to translate pricing logic into production-ready processes Support model performance improvement through data preparation, feature engineering and data mining techniques Monitor production processes and model performance over time, recommending corrective action where required Ensure pricing models are deployed ...

deploy ML models for use cases like fraud detection, risk modeling, and customer analytics Build predictive, classification, clustering, and recommendation models Perform data preprocessing, feature engineering, and model evaluation Design scalable ML pipelines and production-grade solutions Develop NLP and GenAI solutions (RAG, embeddings, LLM-based applications) Deploy … statistical modeling Hands-on experience with Scikit-learn, Pandas, NumPy, XGBoost/LightGBM Experience with TensorFlow/PyTorch/Keras Solid understanding of feature engineering, model tuning, and evaluation metrics Experience with SQL and data handling Knowledge of MLOps (CI/CD, model deployment, monitoring) Experience deploying ...

hire has very meaningful impact.They are looking for a Senior Data Scientist to lead the predictive analytics capability and their critical programme.You’ll design feature engineering pipelines, select and validate classification and regression models, and establish rigorous evaluation methodology (targeting AUC-ROC 0.85) across a 300M+ point education … explainable and production-ready.Core Responsibilities: Predictive Model Development : Build and validate supervised learning models (classification & regression) using complex, multi-source, time-series-influenced data. Feature Engineering : Design scalable feature pipelines across attendance, behaviour, wellbeing, assessment and SEND datasets. Evaluation & Methodology : Establish rigorous evaluation frameworks including ...

taxonomies, citations, metadata, and content from every scientific discipline. Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI. Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization … inference, experimentation, monitoring, and continuous improvement. Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems. Collaborate with engineering, product, UX, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical ...

position responsible for designing, building, and deploying enterprise-grade AI solutions that drive measurable business value across the organisation. Working closely with IT, Data, Engineering, and senior business stakeholders, you will lead the development and implementation of advanced AI solutions, leveraging Large Language Models (LLMs), Retrieval-Augmented Generation … expertise with strategic thinking, taking solutions from concept through to production deployment and adoption. The ideal candidate will have a strong foundation in AI Engineering combined with a robust Data Science background, enabling them to develop, evaluate, and optimise AI solutions that deliver measurable business outcomes. This role would ...

priorities. Identify high-value ML use cases and translate commercial problems into scalable analytical solutions. Lead end-to-end model lifecycle delivery: problem framing, feature engineering, experimentation, validation, deployment, monitoring, and iteration. Establish best practices for experimentation, evaluation, reproducibility, and documentation. Set standards for model governance, explainability, monitoring … financial environment. Machine Learning & Delivery Architect and develop robust ML models (e.g., classification, regression, anomaly detection) using Python and cloud-based tooling. Design scalable feature pipelines in collaboration with Data Engineering. Lead productionisation efforts, including pipeline design, model versioning, and monitoring frameworks. Implement safe deployment strategies (e.g., champion/ ...

Leadership Own end-to-end architecture across data and AI layers: Data ingestion, processing, modelling, semantic layer, and consumption AI platform integration (model lifecycle, feature engineering, inference pipelines) Design modern Lakehouse + AI architecture leveraging Azure and Databricks Define architecture for scalable, governed, and reusable AI-ready data … lineage, security, and responsible AI principles 3. AI Platforms & AI-led Data Migration Design and implement AI Platforms integrating: Model development environments, MLOps pipelines, feature stores, and model serving Lead AI-driven migration strategies , including: Automated schema discovery, data mapping, and transformation using AI accelerators AI-assisted code conversion ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

Responsibilitie sPerform exploratory data analysis (EDA) to uncover trends, anomalies, and opportunitie sDevelop and validate machine learning models (regression, classification, clustering, etc. )Conduct feature engineering, hypothesis testing, and model evaluatio nTranslate business requirements into data science use cases and analytical solution sDeliver actionable insights to drive optimisation, efficiency ...

Leadership Own end-to-end architecture across data and AI layers: Data ingestion, processing, modelling, semantic layer, and consumption AI platform integration (model lifecycle, feature engineering, inference pipelines) Design modern Lakehouse + AI architecture leveraging Azure and Databricks Define architecture for scalable, governed, and reusable AI-ready data … lineage, security, and responsible AI principles 3. AI Platforms & AI-led Data Migration Design and implement AI Platforms integrating: Model development environments, MLOps pipelines, feature stores, and model serving Lead AI-driven migration strategies , including: Automated schema discovery, data mapping, and transformation using AI accelerators AI-assisted code conversion ...

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. ...

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. … 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 ...

Pega CDH adaptive models Strong MLB/RTIM/arbitration logic knowledge Advanced Python & ML development A/B testing, uplift measurement, model governance Feature engineering & real-time vs batch decisioning Git, Agile, cross-functional collaboration NBA Decisioning Specialist Deliver decisioning use cases and personalisation strategies. Key Skills ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysi s.Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale dataset ...

Must-haves 3+ years experience in data science Strong Python and SQL skills Experience building and evaluating predictive models Understanding of data modelling and feature engineering Ability to translate data into clear commercial insight and recommendations Confident communicator - comfortable working directly with clients Consultancy or client-facing experience ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...

build scalable forecasting systems that continuously generate, deploy and improve predictive models. You'll work with large-scale data, applying machine learning, experimentation and feature engineering to improve prediction accuracy and drive better customer outcomes. You'll also help shape product strategy, establish data science best practices ...

SeniorAIEngineerManchester Agrowingtechnology-ledbusinessisseekinga SeniorAIEngineer toplayapivotalroleinshapinganddeliveringitsAIstrategy.Thisisasenior-level,hands-onpositionresponsiblefordesigning,building,anddeployingenterprise-gradeAIsolutionsthatdrivemeasurablebusinessvalueacrosstheorganisation. WorkingcloselywithIT,Data,Engineering,andseniorbusinessstakeholders,youwillleadthedevelopmentandimplementationofadvancedAIsolutions,leveragingLargeLanguageModels(LLMs),Retrieval-AugmentedGeneration(RAG),AIAgents,MachineLearning,andAzureAIservices.Youwillprovidetechnicalleadership,establishbestpractices,andhelpbuildtheorganisationslong-termAIcapability. ThebusinessisinvestingheavilyinAIandislookingforsomeonewhocancombinedeeptechnicalexpertisewithstrategicthinking,takingsolutionsfromconceptthroughtoproductiondeploymentandadoption. TheidealcandidatewillhaveastrongfoundationinAIEngineeringcombinedwitharobustDataSciencebackground,enablingthemtodevelop,evaluate,andoptimiseAIsolutionsthatdelivermeasurablebusinessoutcomes. ThisrolewouldsuitanexperiencedAIprofessionalwhoenjoyssolvingcomplexbusinesschallenges,mentoringothers,influencingtechnicaldirection,anddeliveringinnovativeAIsolutionsatscale. RolePurpose AsaSeniorAIEngineer,youwillleadthedesign,development,deployment,andoptimisationofenterpriseAIsolutions,ensuringtheyaresecure,scalable,maintainable,andalignedtobusinessobjectives. YouwillactasasubjectmatterexpertforAItechnologies,helpingtodefinearchitecture … Strongunderstandingofenterprisecloudarchitecture,includingVNets,EntraID,APIGateways,networking,andsecureintegrations. ExperiencedeployingandmanagingAIworkloadswithinproductionenvironments. StrongSQLanddataengineeringskills,workingwithbothstructuredandunstructureddatasets. AbilitytotranslatecomplexdatasetsintoactionableinsightsandAI-drivenbusinesssolutions. ExperienceimplementingAIgovernance,security,monitoring,andoperationalbestpractices. Excellentcommunication,leadership,stakeholdermanagement,andproblem-solvingskills. HighlyDesirable StrongDataSciencebackground,withexperienceinstatisticalmodelling,predictiveanalytics,featureengineering,experimentation,andmodelevaluation. ExperienceapplyingDataSciencemethodologiestosolvecomplexbusinessproblemsandidentifyopportunitiesforAIadoption. ExperiencewithGenAIOps,LLMOps,MLOps,andAIobservabilityplatforms. ExposuretoComputerVision,OCR,VoiceAI,ConversationalAI,ormultimodalAIsolutions. Experienceworkingwithinoperational,retail,automotive,logistics,orcustomer-centricorganisations. KnowledgeofResponsibleAI,AIgovernanceframeworks,andregulatoryconsiderations. PreviousexperienceleadingAIinitiatives,mentoringengineers,orhelpingdefineAIstrategyandroadmap. ExperiencebuildingorcontributingtoanAICentreofExcellenceorinternalAIcapabilityfunction. ...