Data Scientist

We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles. We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter – packed with personal touches and delivered with care. We’re not just about selling cards or gifts – we’re here to spread joy, spark smiles and make every celebration feel extra special. And with values that guide how we work and support one another, we’ve built a place where people (and ideas) can truly thrive. If you’re looking to make an impact, bring your spark and be part of something meaningful – we’d love to have you on the team. 🌙🐷 Data Scientist | 📍London or Manchester – Hybrid (1–2 office days per week) | 💰Competitive Salary + Benefits About the Role We’re looking for a Data Scientist to join Moonpig, working hybrid from London or Manchester. You’ll build, evaluate and help productionise machine learning solutions that solve real customer and commercial problems across recommendations, personalisation, customer modelling and predictive modelling. This is a hands-on applied Data Science role where you’ll work closely with Product, Engineering, MLOps, Commercial and Marketing. You’ll turn clearly defined problems into practical ML solutions, evaluate whether they’re working and help bring them successfully into production. You’ll have the independence to make sound decisions within your problem space, while being part of a collaborative team that values high-quality, reproducible code and thoughtful experimentation. You’ll also use modern AI-assisted development tools responsibly to improve the speed and quality of delivery. Key Responsibilities Develop and evaluate machine learning models across recommendations, personalisation, customer and predictive modelling. Explore data, engineer useful features and compare modelling approaches, choosing solutions that fit the problem rather than adding unnecessary complexity. Partner with Product, Commercial, Marketing and other stakeholders to understand problems, clarify requirements and translate them into practical Data Science approaches. Apply appropriate offline model evaluation, investigate model behaviour and clearly communicate performance, limitations and trade-offs. Contribute to the design and analysis of A/B tests and other experiments, connecting model performance with customer behaviour and business outcomes. Develop solutions with production use in mind, partnering with Engineering and MLOps to integrate models into ML pipelines and support deployment, monitoring and ongoing improvement. Write tested, modular and maintainable Python and SQL, contributing to shared codebases and reproducible workflows using established software-development and version-control practices. Monitor deployed solutions and investigate model performance, data quality and unexpected behaviour, contributing improvements where needed. Use AI-assisted tooling across coding, analysis, exploration, experimentation and documentation, critically validating outputs to maintain quality. Take part in code and analytical reviews, share knowledge and contribute to reusable tooling, documentation and improvements to Data Science ways of working. About You Experience developing machine learning or advanced analytical solutions in a Data Science, Machine Learning or Advanced Analytics role. Strong practical understanding of supervised machine learning, feature engineering, validation, overfitting and model evaluation, backed by real-world modelling experience. Strong Python and SQL skills, with experience applying both to real-world data and modelling problems. Ability to translate defined customer or business problems into appropriate analytical or machine learning approaches. Experience selecting and applying model evaluation metrics and validation approaches, with an understanding of their strengths and limitations. Experience designing or analysing A/B tests or other controlled experiments, including selecting success metrics and interpreting results. Experience with Git or similar version-control tools and contributing clear, modular and maintainable code to shared codebases. Understanding of testing, reproducibility and good software-development practices. Experience contributing to production machine learning workflows, including an understanding of deployment, monitoring, data quality and the wider model lifecycle. Ability to explain assumptions, methods, results and technical trade-offs clearly to both technical and non-technical audiences. Comfortable independently delivering defined modelling or analytical work and knowing when to seek input on unfamiliar or more complex problems. Comfortable using AI-assi