12 of 12 Remote/Hybrid Feature Engineering Jobs in the UK

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. ...

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 ...

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 ...

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 ...

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 ...

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 ...