About the Role
Join Moonpig Group as a Data Analyst (Product Analytics) and help shape better product decisions through data. Working closely with Product, Design, and Engineering, you’ll explore customer behaviour, support experimentation, and uncover the “why” behind performance.
How will you do this? You’ll explore how customers interact with our platform and help teams design experiments to test the impact of the changes we make on customer behaviour.
This is a great opportunity to combine analytical thinking with real product impact—translating data into clear, actionable recommendations that improve how our products perform and how customers experience them. You’ll also continue to grow your technical toolkit, developing skills in Python, modelling, and causal inference over time.
Key responsibilities
• Contribute to experimentation by helping design, run, and analyse A/B tests, ensuring results are robust and clearly communicated
• Explore and explain drivers of customer behaviour, including conversion and retention, using appropriate analytical techniques
• Support evaluation of AI, recommendation, and personalisation features alongside Data Science and Engineering teams
• Apply analytical and statistical methods using SQL and, where appropriate, Python to explore data and test hypotheses
• Contribute to scalable analytics practices by improving documentation, queries, and reusable analysis
• Conduct behavioural analysis (funnels, cohorts, retention, LTV) to support product insights and recommendations
• Apply causal thinking (with guidance), using experimental and quasi-experimental approaches where appropriate
• Use modern analytical workflows, including AI tools, to improve productivity while maintaining critical thinking
About you
• Familiarity with ecommerce or tools such as Google Analytics is a must have
• Strong SQL skills, including joins, aggregations, and window functions
• Practical experience using Python for analysis (e.g. pandas, notebooks) – nice to have
• Exposure to experimentation, including supporting or running A/B tests
• Ability to structure problems and draw meaningful insights from data
• Clear communication skills, with the ability to explain findings and support recommendations
• Comfortable collaborating with product, design, and engineering stakeholders
• Exposure to personalisation, recommendations, or dynamic pricing is a bonus
• Experience with tools such as Tableau, Mixpanel, Git, Snowflake, or BigQuery is advantageous
• Awareness of causal inference or statistical methods is helpful but not essential
• 1st stage interview – 45 minutes
• 2nd stage interview – 90 minutes (technical/task assessment)
• 3rd stage final interview – 30 minutes (cultural/behavioural assessment)