1 to 25 of 43 Permanent Feature Engineering Jobs in England

analytics environment. This role will focus on building and optimising SAS-based models, supporting the full model development lifecycle from data preparation and feature engineering through to testing, validation, and production deployment. Key Responsibilities Design, develop, and enhance statistical, predictive, and pricing models using SAS Build modelling datasets … feature engineering pipelines, and variable transformations Develop SAS programs using: o Base SAS o SAS Macros o Data Step o SAS SQL Support automated model execution, scoring, and monitoring processes Conduct model testing, validation, back-testing, and performance analysis Analyse large datasets to identify trends, patterns, and business ...

workflows, and develop containerised model services that integrate directly into the OneView platform. This includes hands‐on work with model architectures, feature engineering, model optimisation, performance debugging, schema‐aligned data preparation, and ML‐driven interfaces. This is a role for engineers who want to build and maintain real … real systems, and solve real operational ML problems – not just prototypes. You will work directly with production data, client technical teams, and our internal engineering ecosystem to deliver AI components that are robust, scalable, and deployed into live environments. Key Responsibilities Machine learning engineering Design, train and optimise ...

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 ...

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 ...

performance using appropriate metrics. You will: Explore and visualise data to discover innovative features and potential data sources. Engineer datasets, develop data pipelines, perform feature engineering, and write code to train, deploy, monitor, and run real‐time inferences. Build and monitor ML models, addressing issues such as overfitting … underfitting, data leakage, and drift. You will use your expertise in engineering and DevOps/MLOps to manage our machine learning platforms using AWS SageMaker and other AWS services. You will: Design, build, and improve scalable public cloud‐based machine learning platforms. Develop robust data pipelines and workflows, contributing ...

stakeholders across the business to expand the understanding and impact of machine learning and AI throughout Trainline. We are looking for a Machine Learning Engineering Manager to join our team help shape the future of train travel. You will be part of a highly innovative AI and ML platform … enthusiasts on large-scale production systems, delivering highly impactful products that make a difference to our millions of users. As a Machine Learning Engineering Manager at Trainline you will... 🚄 Lead a high performing team of Machine Learning Engineers working alongside Software Engineers, Data Scientists, Data Engineers and Product Managers ...

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 ...

systems that process high-volume, high-value data and increasingly leverage AI and automation to deliver measurable customer and business outcomes. We value pragmatic engineering, clear thinking, and continuous learning. Our teams are small, autonomous, and outcome-focused, with a strong emphasis on quality, ownership, and collaboration. … platform scalability, operational excellence and maturity, and cost-efficient growth to support our long-term strategy and enterprise ambitions. The Role AI Platform Architecture & Engineering Own the design, build, and evolution of Infinity’s AI platform - the infrastructure layer that underpins AI-driven product capability and AI-assisted engineering ...

technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‐grade standards. You will contribute … compliance framework. This is a highly hands‐on, highly technical, principal‐level role that combines architectural vision with deep practical expertise in ML engineering and AWS-native MLOps. Key Responsibilities Technical Leadership & Architecture Define the end‐to‐end ML architecture for the matching platform, including data pipelines, model training ...

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 ...

features, model selection, architecture decisions Conduct exploratory data analysis and communicate insights Collaborate with data engineers, architects, and business analysts on integrated solutions Build feature engineering pipelines and automated data preparation workflows Design AI solutions for commercial transformation including pricing optimization, customer segmentation, and revenue management Develop scalable … proposals and technical assessments for new opportunities and internal knowledge transfer Essential Qualifications Degree or equivalent certification in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field Proven experience building and implementing LLM-based solutions (GPT, Claude, Llama, Mistral, or similar) Hands‐on experience with at least ...

model selection, and architecture decisions Conduct exploratory data analysis and communicate insights Collaborate with data engineers, architects, and business analysts on integrated solutions Build feature‐engineering pipelines and automated data preparation workflows Design AI solutions for commercial transformation including pricing optimization, customer segmentation, and revenue management Develop scalable … proposals and technical assessments for new opportunities and internal knowledge transfer Essential Qualifications Degree or equivalent certification in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field Proven experience building and implementing LLM‐based solutions (GPT, Claude, Llama, Mistral, or similar) Hands‐on experience with at least ...

solutions and regulatory reporting — ensuring teams across the business can trust and use data with confidence. You’ll collaborate closely with colleagues in Data Engineering, Data Science, Pricing, Platform Engineering and MLOps, acting as a trusted partner and technical authority. Together, you’ll define clear standards … self‐service analytics and AI/ML model consumption. Collaborate with ML Ops and Data Science teams to define data product standards and feature engineering patterns. Evaluate and lead adoption of emerging Azure and Databricks capabilities — including Microsoft Fabric, OneLake and DirectLake — where they advance the data architecture. ...

solutions and regulatory reporting — ensuring teams across the business can trust and use data with confidence. You’ll collaborate closely with colleagues in Data Engineering, Data Science, Pricing, Platform Engineering and MLOps, acting as a trusted partner and technical authority. Together, you’ll define clear standards … self‐service analytics and AI/ML model consumption. Collaborate with ML Ops and Data Science teams to define data product standards and feature engineering patterns. Evaluate and lead adoption of emerging Azure and Databricks capabilities — including Microsoft Fabric, OneLake and DirectLake — where they advance the data architecture. ...

exploration into advanced techniques, including LLMs, RAG architectures, and Generative AI applications to create new forms of automated analysis and augmented intelligence products. Production Engineering & MLOps : Translate validated prototypes into robust, production‐ready specifications, and lead the implementation of MLOps best practices (CI/CD, monitoring, serving) required … reliable deployment of models. Complex Data & Feature Engineering : Deeply explore complex, multi‐modal data (e.g., high‐dimensional data, text, time series) defining the necessary features and data pipelines to support highly accurate experimental models for strategic analysis. Cross‐Functional Collaboration : Work closely with the Product Manager, Data Scientists ...

client‐facing LLM‐powered experiences used by business travelers worldwide. We are looking for a Machine Learning Engineer II to join our Product AI Engineering team. You will help us design, build, and optimize intelligent systems. This is not just a modeling role, you’ll own the full lifecycle … concepts and steps involved in working with real‐world large data sets: from domain‐specific problem understanding, data preparation, travel‐related data processing, feature engineering, data structures for ML, data pipelining, modeling, offline evaluation, online evaluation, and monitoring. Strong knowledge of hands‐on practice in Python. Familiarity with ...

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/ ...

Spring framework, JDBC and database internals. Experience with CI/CD pipelines and distributed and concurrent systems. Experience with machine learning basics (data pipelines, feature engineering, recall/precision, familiarity with machine learning systems in production) is a huge plus. A strong product mindset and passion for user … work – a practical guide DEI @ Wise Wise Tech Stack (2025 update) See what it's like to work at Wise London! Our Engineering career map Wise Engineering – https://medium.com/wise-engineering What do we offer: Starting salary: £87,500 - £111,000 + RSUs Wise ...

models Collaborate with senior team members to define cognitive computing strategies and guide the full AI project lifecycle Conduct in‐depth exploratory data analysis, feature engineering, and model selection for structured and unstructured data Utilize advanced analytics techniques, including NLP and ML, to extract insights and drive decision ...

Company Ion recruitment Location Surrey, UK (Office based) Overview A global software company is evolving its core engineering platforms by embedding machine learning and applied AI into high‐performance simulation and modelling tools used worldwide. This is a hands‐on applied AI role — focused on building and deploying … solutions inside production‐grade engineering systems, not isolated research or experimental prototypes. Responsibilities Create, deploy, and maintain machine learning models in production engineering software systems. Own the full ML pipeline: data preparation, feature engineering, training, evaluation, and optimisation. Translate complex scientific/engineering problems into ...

Join our global Quantitative Trading & Research (QTR) team, where you’ll apply your expertise in Derivatives Modelling, Financial Engineering, Data Science, and Quantitative Development. As part of JP Morgan’s leading QTR Group, you’ll innovate with unique analytics and mathematical models, enhancing business practices through automation. We develop … limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best ...

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 ...

limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best … resourcing via data‐driven analysis, scenario studies, and clear articulation of trade‐offs. Lead development of ML/AI solutions end‐to‐end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value. Uphold standards for documentation, reproducibility ...