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About the Role
The Customer Data Science team at Burberry is seeking an early-career Data Scientist to enhance customer experiences through personalization. This role involves applying statistical modeling, machine learning, and AI techniques to business challenges, working with cross-functional teams to build scalable solutions that deepen customer understanding and improve marketing effectiveness.
Responsibilities
- Develop statistical models, machine learning solutions, and data-driven tools for customer and business objectives.
- Explore and prepare new data sources, assess data quality, and create features for modeling.
- Apply techniques like propensity modeling, causal inference, and experimentation to understand customer behavior and marketing impact.
- Contribute to product recommendation, discovery, and client relationship solutions for personalized customer experiences.
- Collaborate with data scientists and engineers to build scalable, production-ready solutions.
- Monitor and evaluate production models using technical and business metrics for improvement opportunities.
- Optimize existing models and analytics solutions through a test-and-learn approach.
- Translate business questions into analytical frameworks, methodologies, and practical solutions.
- Generate insights and recommendations to inform strategic and operational decisions.
- Present analytical methods, findings, and limitations to technical and non-technical stakeholders.
- Identify opportunities to improve models, processes, and team workflows.
- Explore and apply new data science and AI technologies that deliver value.
Requirements
- Master's degree or PhD in a quantitative discipline (Data Science, Mathematics, Statistics, Econometrics, Computer Science, Physics, Engineering) or equivalent technical knowledge.
- Master's-level project, placement, or internship experience, or approximately one year of relevant data science experience.
- Experience applying statistical analysis, machine learning, or data science techniques to practical problems.
- Sound understanding of mathematics, statistics, experimental design, and model evaluation.
- Practical experience developing, testing, and interpreting statistical or machine learning models.
- Exposure to specialist areas such as time series, recommendation systems, customer journey modeling, causal inference, deep learning, or large language models.
- Solid programming foundation with practical experience using Python and SQL.
- Familiarity with libraries/technologies like Pandas or PySpark is advantageous.
- Understanding of collaborative development practices, including version control tools like Git.
- Exposure to Python packaging tools like Poetry is welcomed but not essential.
- Logical and considered approach to problem-solving with curiosity for new analytical methods.
- Ability to translate business requirements into structured analytical questions and practical approaches.
- Collaborative working style and ability to contribute effectively across technical and business teams.
- Clear communication skills, with the ability to explain complex analysis to different audiences.
- Commitment to continuous learning in data science, machine learning, and AI.
Skills
- Statistical Modelling
- Machine Learning
- AI Techniques
- Propensity Modelling
- Causal Inference
- Experimentation
- Product Recommendation
- Discovery Algorithms
- Client Relationship Intelligence
- Python
- SQL
- Pandas
- PySpark
- Git
- Poetry
Experience Level
- Early-career
- Approximately one year of relevant experience
Education Level
- Master's degree
- PhD
About the Company
- Burberry believes creativity opens spaces and aims to unlock the power of imagination to push boundaries and open new possibilities.
- The company provides an environment for creative minds from diverse backgrounds to thrive.
- Burberry is a purposeful, values-driven brand committed to being a force for good, creating sustainable luxury, driving industry change, and championing communities.
Equal Opportunity
- Burberry is an Equal Opportunities Employer and treats all applications equally, recruiting purely on the basis of skills and experience.