Data Scientist

The role

You will build, evaluate and help put machine learning solutions into production for recommendations, personalisation, customer modelling and predictive modelling. You will work with Product, Engineering, MLOps, Commercial and Marketing.

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.
• Partner with Product, Commercial, Marketing and other stakeholders to understand problems and translate them into Data Science approaches.
• Apply offline model evaluation, investigate model behaviour and communicate performance, limitations and trade-offs.
• Contribute to the design and analysis of A/B tests and other experiments.
• Develop solutions for production use, working with Engineering and MLOps on pipelines, deployment and monitoring.
• Write tested, modular and maintainable Python and SQL, using version control.
• Monitor deployed solutions and investigate model performance, data quality and unexpected behaviour.
• Use AI-assisted tooling across coding, analysis, experimentation and documentation, validating outputs.
• Take part in code and analytical reviews, share knowledge and contribute to reusable tooling.

Required qualifications

• 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.
• Strong Python and SQL skills.
• Ability to translate defined customer or business problems into analytical or machine learning approaches.
• Experience selecting and applying model evaluation metrics and validation approaches.
• Experience designing or analysing A/B tests or other controlled experiments.
• Experience with Git or similar version-control tools.
• Understanding of testing, reproducibility and good software-development practices.
• Experience contributing to production machine learning workflows, including deployment, monitoring, data quality and the model lifecycle.
• Ability to explain assumptions, methods, results and trade-offs to technical and non-technical audiences.
• Comfortable independently delivering defined modelling or analytical work.
• Comfortable using AI-assisted development tools and critically evaluating their outputs.

Nice to have

• Experience in B2C e-commerce, retail or a high-volume digital environment.
• Experience with recommendation or personalisation systems.
• Exposure to customer modelling approaches such as propensity, uplift or customer lifetime value modelling.
• Experience applying LLMs, embeddings or other generative AI capabilities to practical problems.
• Experience with cloud-based data or machine learning platforms, particularly AWS.
• Familiarity with analytics engineering tooling such as dbt.
• A degree in Statistics, Mathematics, Economics, Computer Science or another relevant quantitative discipline.

Benefits

• Competitive pay and bonuses.
• Generous pension plans and staff discounts.
• Private healthcare (UK) and mental health support.
• Generous holidays.
• Hybrid working (1-3 days in office, depending on role/team).
• Up to 20 days of international working.
• Learning allowances, coaching and development programs.

Before you apply

• The interview process includes a recruiter screening, Hiring Manager interview, Technical Screening, Technical Interview follow-up and Final Round.
• A technical screening is part of the hiring process.

About the company

Moonpig Group is home to Moonpig, Greetz, Red Letter Days and Buyagift, and helps people stay close through cards and gifts. The group describes itself as leading the online gifting revolution.