Data Scientist

Overview

In this role, you apply statistical modelling and machine learning to deepen understanding of customer behaviour and improve personalised experiences across touchpoints. You will work with data scientists, engineers and cross-functional partners to build scalable, production-ready models. The role focuses on propensity, causal inference, recommendations and client insights to inform business decisions. You will explore data sources, validate models and communicate findings to technical and non-technical stakeholders. This is a chance to contribute to Burberry’s sustainable luxury mission while shaping customer-driven product experiences.

Responsibilities

• Develop statistical models and ML solutions aligned with business objectives
• Explore data sources and create features for modelling
• Apply propensity modelling, causal inference and experimentation
• Contribute to product recommendations, discovery and client relationship solutions
• Collaborate with data scientists and engineers to deliver scalable, production-ready solutions
• Monitor and evaluate models in production using technical and business metrics
• Optimize models through a structured test-and-learn approach
• Translate business questions into analytical frameworks and practical solutions
• Generate insights and recommendations to inform strategy
• Present methods and findings to diverse stakeholders
• Identify opportunities to improve models, processes and ways of working
• Stay updated on data science and AI developments and apply them where valuable

Key requirements

• Master’s degree or PhD in a quantitative discipline or equivalent technical knowledge
• Master’s-level project or approximately one year of relevant experience in data science
• Experience applying statistical analysis, ML or data science techniques to practical problems
• Strong foundation in mathematics, statistics, experimental design and model evaluation
• Hands-on experience developing, testing and interpreting statistical or ML models
• Exposure to areas such as time series, recommendation systems, causal inference, deep learning or large language models
• Solid programming foundation with Python and SQL
• Familiarity with Pandas or PySpark is advantageous
• Understanding of Git and collaborative development practices
• Curiosity to explore new analytical methods and translate business requirements into analytical questions
• Collaborative working style and ability to work across technical and business teams
• Clear communication skills to explain complex analysis to varied audiences
• Commitment to continuous learning in data science and AI

• collaborative
• curious/problem-solving
• excellent communication
• Python
• SQL
• Pandas

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