Burberry Data Scientist
- 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.
- 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.
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