25 of 25 Feature Engineering Jobs in the UK

deliver production-grade ML systems for a leading digital gaming and gambling platform. This is a hands-on role combining data science expertise with engineering skills - you'll build models, optimise algorithms, and deploy solutions at scale to enhance customer engagement and decisioning. You'll work closely with Data … science & modelling: Develop, validate, and optimise predictive models using advanced ML algorithms (e.g., gradient boosting, logistic regression, ensemble methods). End-to-end ML engineering: Deploy models as APIs, batch jobs, and streaming services; implement CI/CD, monitoring, and rollback strategies. Feature engineering & pipelines: Build scalable ...

tools. * Write clean, efficient Python code for data processing, modelling, and experimentation. * Extract, manipulate, and analyse data using SQL across structured data environments. * Support feature engineering, model optimisation, and validation activities. * Collaborate with cross functional teams to understand requirements and deliver insights. * Communicate findings clearly to technical … Approximately 5 years of hands on experience in data science or a closely related field. Nice to Have Skills * Experience with Databricks for data engineering or ML workflows. * Familiarity with Azure ML, MLflow, or similar MLOps tooling. * Exposure to feature engineering best practices and model lifecycle management. ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

building the future of population health management. As a Senior Machine Learning Engineer, you will sit at the intersection of data science, platform engineering, and production systems. Your mission is to design and operate the foundations that allow ML, analytics, and agentic AI systems to move safely and reliably … from experimentation into regulated healthcare production environments on Google Cloud Platform. You will be the primary interface between Data Science and Platform Engineering, enabling rapid iteration while enforcing production and compliance standards. Responsibilities Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns ...

machine learning and deep learning models. Work on AI/ML projects including NLP, computer vision, recommendation systems, and generative AI. Perform data cleaning, feature engineering, and exploratory data analysis (EDA). Build and manage data pipelines and model training workflows. Deploy models into production and monitor performance. … Collaborate with Product, Engineering, and Business teams to translate business problems into AI solutions. Conduct model evaluation, A/B testing, and performance tuning. Document models, experiments, and technical processes. Required Skills & Qualifications:- Classic Machine learning (Regression, predictive Analysis, Classification, Clustering) Machine learning Model Optimisation Strong proficiency in Python ...

across the business. They are looking for a Senior AI Engineer to lead the delivery of end-to-end AI products, working across software engineering, machine learning, and LLM-based systems. This is a hands-on role focused on shipping real solutions into production that directly impact customers, teams … commercial outcomes. The RoleYou will join a cross-functional AI function working at the intersection of software engineering, data, and machine learning. Your focus will be on turning business problems into production-ready AI systems, from early discovery through to deployment, monitoring, and continuous improvement. You will work closely ...

Drive experimentation frameworks, hypothesis testing, model validation, and statistical rigor Ensure robustness, generalisation, bias mitigation, and explainability in deployed models Provide technical direction on feature engineering strategies and model performance enhancement Collaborate with engineering teams to transition models into scalable production systems Mentor data scientists and uphold … 8+ years of hands-on experience in machine learning and data science Advanced degree (Master’s or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline Proven experience building and deploying advanced ML and deep learning models in enterprise environments Deep understanding of algorithm selection, model ...

applied across the platform. Requirements 4-7+ years of professional experience in Data Science/ML roles Strong data science foundations (EDA, feature engineering, model evaluation, validation) Hands-on experience with ML models in production Cloud & MLOps exposure (GCP preferred; AWS/Azure acceptable) Experience with GenAI … plus, curiosity required) Responsibilities Build, train, and improve ML models on large-scale blockchain datasets Take models from exploration to production, working closely with engineering Apply GenAI/LLM approaches where they add real product value Maintain high DS/ML standards across experimentation, monitoring, and iteration Contribute ...

adapted across multiple operational entities globally. Key Responsibilities Design and implement predictive and prescriptive models to optimize MRO operations. Perform exploratory data analysis, feature engineering, and model validation. Collaborate with data engineering teams to ensure high-quality, production-ready datasets. Communicate findings and recommendations to senior business ...

operational use, including synthetic data generation. Build and productionise ML/NLP solutions for legal and business use cases to enable better decision making. Feature Engineering & Integration : Develop new features, integrate data from internal/external sources, and create unified views to enhance performance and outcomes. Pipeline Development ...

models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development. Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud‐native … relevance. Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement. Cross‐Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals. Research & Innovation: Stay informed on advances in machine learning ...

modern cloud technologies, largescale data systems and cuttingedge tools to support predictive analytics, machine learning initiatives and enterprise reporting. If you're excited by engineering challenge, modern data platforms and solving realworld problems, you'll be a great fit. This is a hybrid role. You will be expected … sessions and department days. Please click here to download the Job description for this role. About you You're an experienced and adaptable data engineering professional who enjoys creating welldesigned, efficient and reliable data solutions. You're comfortable working with complex systems, experimenting with new tools and collaborating with ...

solutions that move into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and ML products, contributing to model development, data pipelines, and deployment workflows that power business-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support ML-driven products and insights 🧠 Assist with feature engineering, experimentation, and model optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

things that go into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and AI products, contributing to model development, data pipelines, and deployment workflows that power mission-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support AI-driven products and insights 🧠 Assist with feature engineering, model experimentation, and performance optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

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 ...

skills, and a results-driven mindset Role responsibilities: Build, tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over ...

optimisation project. You will design and implement predictive and prescriptive models for Maintenance, Repair, Overhaul AI Solutions. Performing exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modelling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback and operational ...

multi-tenant SaaS platform. Create ETL pipelines for large, complex property datasets. Monitor, tune, and iterate on model performance. Collaborate with product and engineering teams on ML-driven features. Document solutions and communicate clearly with technical and non-technical stakeholders. Key Skills 4+ years as a Machine Learning Engineer … similar role. Strong Pythonengineering skills. Experience deploying ML models in production. Understanding of multi-tenant SaaS architecture. Solid ETL, data processing, and feature engineering experience. Strong statistical and analytical capabilities. Experience in start-up or scale-up environments. Desirable MLOps tools (MLflow, Kubeflow, Sagemaker, etc.) Experience in PropTech ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

data interchange methods. - Knowledge of database environments (e.g., MS SQL, Dataverse, OneLake). - Experience working with or supporting machine learning models, including data preparation, feature engineering and testing. - Understanding of cloud services such as Azure Functions, Logic Apps, Azure AI Foundry or similar. - Strong coding skills, ideally ...

compute performance. Deploy pipelines using CI/CD and IaC. Deliver curated datasets and semantic layers for BI, analytics, and ML. Support ML feature engineering and model delivery. Define scalable data architecture patterns and integration designs (ingestion, storage, transformation, analytics). Develop and maintain conceptual, logical, and physical … support scalability and strategic needs. Personal Requirements: 3–5+ years’ experience with Microsoft SQL Server (2016+) and SSIS in production environments. Strong SQL Server engineering: advanced T-SQL, indexing, performance tuning, execution plans, statistics, partitioning, tempdb optimisation. Proven experience with ETL/ELT development and design patterns (incremental loads ...

Python, R, SQL, Excel or Power BI. - A good understanding of statistical methods and an interest in machine learning techniques such as regression, feature engineering or natural language processing. - The ability to communicate analytical concepts clearly to nontechnical colleagues. - A collaborative mindset and confidence working with stakeholders across ...

off. Deploy and monitor models using MLOps approaches (Docker, CI/CD, cloud platforms like AWS/Azure/GCP). Collaborate closely with engineering and product teams to integrate ML solutions seamlessly into production. Work with modern ML frameworks and libraries such as PyTorch, TensorFlow, HuggingFace, and LLMs … models. Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps practices. Demonstrated ability to work with complex real-world datasets and perform feature engineering. Familiarity with modern ML frameworks and libraries, including PyTorch, TensorFlow, HuggingFace, and LLMs/SLMs. Excellent problem-solving skills and ability to work ...

rail in real time to maximise approval probability and net value. Key deliverables: routing decision API, signed short‐lived Authorization Token, idempotent retry flow, feature pipelines, model monitoring and A/B/holdout experiments to prove approval uplift. Agentic Treasury — project summary What it is: an orchestration … procedures). What you’ll also own as a co‐founder first 3–6 months Shape the payments product portfolio — one‐page strategy, MVP feature lists and unit economics covering Smart Routing, Agentic Treasury and follow‐on ideas as appropriate. Validate partners rails and regulatory constraints — map PSPs, acquirers ...