Location
London

Hours
Full Time

Salary
Negotiable

About the Role
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, customers and communities. As a purposeful, values-driven brand, we are committed to being a force for good, creating the next generation of sustainable luxury, driving industry change and championing our communities.

The Customer Data Science team is looking for an early-career Data Scientist to help create more relevant and personalised experiences across every customer touchpoint. This role suits someone with master’s-level knowledge or approximately one year of relevant experience, ready to apply statistical modelling, machine learning and emerging AI techniques to meaningful business challenges.

Working closely 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 propensity and causal modelling, product recommendations, discovery algorithms and client relationship intelligence.

Key responsibilities include developing robust statistical models and machine learning solutions, exploring and preparing new data sources, applying techniques such as propensity modelling and causal inference, contributing to recommendation and client relationship solutions, collaborating to build production-ready solutions, monitoring and optimising models, translating business questions into analytical frameworks, generating insights to inform decisions, presenting findings clearly to diverse audiences, identifying improvement opportunities, and exploring new data science and AI technologies to deliver value.

Experience
- Master’s-level project, placement or internship experience, or approximately one year of relevant experience in data science or a closely related role.
- Practical experience applying statistical analysis, machine learning or data science techniques to real-world problems.
- Exposure to specialist areas such as time series, recommendation systems, customer journey modelling, causal inference, deep learning or large language models is advantageous.
- Practical experience developing, testing and interpreting statistical or machine learning models.
- Familiarity with collaborative development practices including version control tools such as Git.

About you
- A logical and considered approach to problem-solving with curiosity to explore new analytical methods.
- Ability to translate business requirements into structured analytical questions and practical solutions.
- Collaborative working style with the ability to contribute effectively across technical and business teams.
- Clear communication skills to explain complex analysis to technical and non-technical audiences.
- Commitment to continuous learning and staying informed about developments in data science, machine learning and AI.

Qualifications
- 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.
- Solid programming foundation with practical experience using Python and SQL.
- Familiarity with relevant libraries and technologies such as Pandas or PySpark is advantageous.
- Exposure to Python packaging tools such as Poetry is welcomed but not essential.