Data Scientist

Overview

In this role you apply statistical modelling, machine learning and AI to create personalized customer experiences at Burberry. You’ll work with data scientists, engineers and cross-functional partners to build scalable, production-ready solutions that deepen understanding of customer behaviour and guide marketing decisions. The work spans propensity modelling, recommendations, discovery algorithms and client relationship intelligence, with impact on both product and business outcomes. This is an opportunity to shape data-driven decisions in a globally recognized, sustainability-minded luxury brand.

Responsibilities

• Develop robust statistical models and ML solutions aligned with business objectives
• Prepare and feature-engineer data from new sources to support modelling
• Apply propensity modelling, causal inference and experimentation to measure impact of outreach
• Contribute to product recommendations, discovery and client relationship solutions
• Collaborate with data scientists and engineers to deliver scalable, reliable, production-ready solutions
• Monitor model performance and identify improvement opportunities using business metrics
• Implement structured test-and-learn approaches to optimise analytics solutions
• Translate business questions into analytical frameworks and actionable insights
• Present methods, findings and limitations to technical and non-technical stakeholders
• Identify opportunities to improve models, processes and ways of working across the team
• Explore new data science and AI developments and apply them where value is delivered

Key requirements

• Master’s degree or PhD in a quantitative discipline (e.g., Data Science, Mathematics, Statistics, Econometrics, Computer Science) or equivalent technical knowledge
• Master’s level project/internship experience or ~1 year in data science
• Experience applying statistical analysis, ML or data science to practical problems
• Solid understanding of mathematics, statistics, experimental design and model evaluation
• Practical experience developing, testing and interpreting models
• Exposure to areas such as time series, recommendation systems, customer journey modelling, causal inference, deep learning or large language models
• Strong programming foundation in Python and SQL
• Familiarity with Pandas or PySpark advantageous
• Understanding of Git and collaborative development practices; Poetry welcomed but optional
• Clear analytical thinking, problem-solving, and curiosity to adopt new methods
• Strong communication skills to explain complex analysis to diverse audiences
• Commitment to continuous learning in data science, ML and AI
• Equal opportunities mindset

• collaborative teamwork
• clear communication
• curiosity and problem-solving
• Python
• SQL
• Pandas

…