8 of 8 Remote/Hybrid Feature Engineering Jobs in London

pure management role. They are looking for a strong Data Scientist first, someone comfortable across the full lifecycle from ingestion and feature engineering through to modelling and deployment. Their data estate has evolved over time and is currently spread across multiple siloed systems with differing structures and standards. … systems, and proactively define a clear data and AI roadmap. You will lead a Data team of 5-6 across Data Science and Data Engineering, raising standards while still contributing directly to predictive models and AI driven tools. This is a genuine opportunity to bring structure, clarity and technical ...

Develop and support analytical and statistical models using Python 🛠 Work with real-world energy and environmental datasets, including data cleaning, validation, and feature engineering ☁️ Use AWS services to support data storage, processing, and analytics workflows 🗄 Query and manipulate structured datasets using SQL 📊 Build reports, dashboards, and visualisations that … tailored to experience, including bonus, pension, and generous annual leave ✅ What we’re looking for: 🎓 A degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field 💼 0–2 years of experience in data analysis or data science (graduate roles, internships, placements, or academic projects welcome) 🐍 Strong ...

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 ...

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 ...

BI. Secure: Implement rigorous data security, GDPR compliance, and client ring-fencing (Row-Level Security). Innovate: Create SQL and PySpark notebooks to prepare feature engineering datasets for AI/ML models. Collaborate: Work independently via Jira/GitHub, maintaining high CI/CD and documentation standards. What … Bring 5+ years of Data Engineering experience. Expertise: Deep knowledge of Snowflake and Microsoft Fabric . Tech Stack: Advanced SQL , Python , and PySpark (Notebooks). Modelling: Strong relational data modelling and cloud cost optimization skills. DevOps: Experience with GitHub, CI/CD, and workflow orchestration. Randstad Technologies is acting ...

This role is ideal for someone who thrives in building modern data platforms, enabling high‐quality data pipelines, and collaborating closely with Data Science, Engineering, and Product teams to deliver scalable data solutions. The successful candidate will play a key role in designing, developing, and maintaining the data infrastructure … pipelines (batch and streaming). Develop and optimise ETL/ELT workflows to ensure efficient data ingestion and transformation. Build and maintain data models , feature stores, and data marts to support analytics and AI initiatives. Work with cloud platforms ( AWS, Azure, or GCP ) to design and deploy data solutions. ...

platform. The Opportunity This is a hands-on leadership role where you will lead a team of 5–6 across Data Science and Data Engineering while remaining technically involved in modelling, machine learning deployment and architectural decisions. The platform runs on AWS and includes large-scale crawlers ingesting significant … Designing and improving a scalable AWS based data platform Rationalising fragmented data across multiple systems Leading the full machine learning lifecycle from ingestion and feature engineering through to deployment Improving neural network and rule based matching systems used to identify the same entities across different sources Embedding robust ...

PyTorch, TensorFlow, or scikit-learn. LLM Experience: Proven experience working with large language models (e.g. GPT, Claude, Gemini) in production environments, including prompt engineering, evaluation, and safety considerations. Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications. Modern AI Tooling … including CI/CD, model versioning, observability, and retraining pipelines. Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering. User-Centred Mindset: Ability to translate complex AI capabilities into intuitive and seamless product experiences. Collaborative Approach: Thrives in cross-functional, creative teams ...