Data Scientist

Department: Strategy & Data & Analytics

City: London

Location: GB

Introduction

At Burberry, we believe creativity opens spaces. Our purpose is to unlock the power of imagination to push boundaries and open new possibilities for our people, our customers and our communities. This is the core belief that has guided Burberry since it was founded in 1856 and is central to how we operate as a company today.

We aim to provide an environment for creative minds from different backgrounds to thrive, bringing a wide range of skills and experiences to everything we do. As a purposeful, values-driven brand, we are committed to being a force for good in the world as well, creating the next generation of sustainable luxury for customers, driving industry change and championing our communities.

Job Purpose

The Customer Data Science team at Burberry is looking for an early-career Data Scientist to help create more relevant and personalised experiences across every customer touchpoint. This role is well suited to someone with master’s-level knowledge or approximately one year of relevant experience who is ready to apply statistical modelling, machine learning and emerging AI techniques to meaningful business challenges. Working with data scientists, engineers and cross-functional stakeholders, you will build scalable solutions that deepen our understanding of customer behaviour, preferences, purchase intent and response to marketing activity. Your work will contribute to areas including propensity and causal modelling, product recommendations, discovery algorithms and client relationship intelligence.

Responsibilities

Develop robust statistical models, machine learning solutions and data-driven tools that support customer and business objectives.
Explore and prepare new data sources, assess data quality and create relevant features for modelling and analysis.
Apply techniques including propensity modelling, causal inference and experimentation to understand customer behaviour and measure the impact of customer outreach.
Contribute to product recommendation, discovery and client relationship solutions that create more relevant customer experiences.
Collaborate with data scientists and engineers to build scalable, reliable and production-ready solutions.
Monitor and evaluate models in production, using technical and business measures to identify opportunities for improvement.
Optimise existing models and analytics solutions through a structured test-and-learn approach.
Translate business questions into clear analytical frameworks, methodologies and practical solutions.
Generate reliable insights and recommendations that inform strategic and operational decisions.
Present analytical methods, findings and limitations clearly to technical and non-technical stakeholders.
Identify opportunities to improve models, processes and ways of working across the team.
Explore relevant developments in data science and AI, applying new technologies where they can deliver meaningful value.

Personal Profile

A master’s degree or PhD in a quantitative discipline, such as Data Science, Mathematics, Statistics, Econometrics, Computer Science, Physics or Engineering, or equivalent technical knowledge.
Master’s-level project, placement or internship experience, or approximately one year of relevant experience in data science or a closely related role.
Experience applying statistical analysis, machine learning or data science techniques to practical problems in an academic or commercial setting.
A sound understanding of mathematics, statistics, experimental design and model evaluation.
Practical experience developing, testing and interpreting statistical or machine learning models.
Exposure to one or more specialist areas, such as time series, recommendation systems, customer journey modelling, causal inference, deep learning or large language models.
A solid programming foundation, with practical experience using Python and SQL.
Familiarity with relevant libraries and technologies such as Pandas or PySpark would be advantageous.
An understanding of collaborative development practices, including version control tools such as Git.
Exposure to Python packaging tools such as Poetry would be welcomed but is not essential.
A logical and considered approach to problem-solving, with the curiosity to explore new analytical methods.
The ability to translate business requirements into structured analytical questions and practical approaches.
A collaborative working style and the ability to contribute effectively across technical and business teams.
Clear communication skills, with the ability to explain complex analysis to different audiences.
A commitment to learning and keeping informed about developments in data science, machine learning and AI.

Burberry is an Equal Opportunities Employer and as such, treats all applications equally and recruits purely on the basis of skills and experience.