Senior 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. 🌙🐷 Senior Data Scientist | 📍London or Manchester – Hybrid (1–2 office days per week) | 💰Competitive Salary + Benefits About the Role We’re looking for a Senior Data Scientist to join Moonpig, working hybrid from London or Manchester. You’ll own high-value machine learning problems end-to-end, from identifying opportunities and shaping problems through to technical delivery, production and measurable customer or commercial impact. This is a hands-on senior individual-contributor role with significant technical and product ownership. You’ll work across recommendations, personalisation, customer modelling and predictive modelling, partnering closely with Product, Engineering, MLOps, Commercial and Marketing to understand where Data Science can create the most value and how we should measure success. You’ll have the space to navigate ambiguity and make sound technical decisions independently. You’ll design robust offline and online evaluation, own meaningful models and ML components throughout their lifecycle, and use evidence to help shape product and business decisions. Key Responsibilities Own machine learning problems, models and components end-to-end across recommendations, ranking, personalisation, customer modelling and predictive modelling. Partner with Product, Commercial, Marketing and other stakeholders to identify high-value opportunities, shape ambiguous problems and determine whether Data Science is the right intervention. Independently select, build and improve modelling approaches, using feature engineering, tuning and appropriate algorithmic choices to improve performance. Design robust offline evaluation strategies, selecting metrics that reflect problem-specific behaviour and trade-offs rather than relying solely on generic model-performance measures. Design and support online experiments to evaluate real-world impact, working with Product and Analytics partners to define success metrics, guardrails and appropriate interpretation of results. Own outcomes beyond model delivery: follow solutions through production and experimentation, determine whether they are creating the intended customer or commercial impact and drive iteration where they are not. Design components of machine learning systems, such as feature-generation pipelines, model-scoring logic and inference workflows, working closely with Engineering to integrate solutions into production. Collaborate with MLOps to deploy models and ensure appropriate monitoring, retraining and operational processes are in place, addressing issues such as drift, data-quality problems and performance degradation. Write high-quality, tested and maintainable Python and SQL, contributing robust and reproducible solutions to shared production codebases. Build practical AI-powered features where appropriate, such as solutions using prompts, embeddings or other generative AI capabilities, and evaluate their outputs systematically. Use AI-assisted development tools to improve coding, analysis, experimentation and documentation, critically evaluating outputs and identifying opportunities to improve team workflows. Communicate technical decisions, model behaviour, trade-offs and recommendations clearly, using evidence to influence product and business decisions and prioritisation. Contribute to the wider Data Science capability through informal mentorship, peer review, knowledge sharing, reusable tooling and improvements to technical practices and ways of working. About You Strong experience developing and delivering machine learning solutions in a Data Science, Machine Learning or closely related role, including ownership of models or substantial ML components. Strong practical understanding of supervised machine learning, feature engineering, model selection, tuning, validation and evaluation, with experience independently improving model performance. Strong Python and SQL skills, with experience developing robust Data Science solutions in shared production codebases. Ability to take ambiguous customer or business problems, determine an appropriate Data Science