Data Scientist

Role Overview

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.

What You Will Do

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.

Why It Might Be a Fit

You’ll use evaluation and experimentation to understand whether solutions are making a difference, while collaborating across Data Science, Product, Engineering, MLOps and our business teams.

Requirements

• 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-assisted development tools for coding, analysis or experimentation, with the judgement to critically evaluate their outputs.
• Awareness of data quality, privacy, fairness, security and customer-experience considerations when developing data-driven products and solutions.

Benefits

• Competitive Pay & Bonuses
• Generous pension plans
• Staff discounts
• Private healthcare (UK)
• Mental health support
• Flexible Working & Time Off
• Generous holidays
• Hybrid working (1-3 days in office, depending on role/team)
• Up to 20 days of international working
• Career Growth
• Learning allowances
• Coaching & development programs

Tagged as: Data Analysis, Python, SQL