Data Scientist - Personalization & Customer Insights

FashionUnited Group; Greater London; Hybrid; GBP 45,000 - 65,000; Full time

Job summary

Burberry is seeking an early-career Data Scientist to build scalable models and analytics across customer touchpoints. You will apply statistical modelling, machine learning and emerging AI techniques to meaningful business challenges, collaborating with data scientists, engineers and cross-functional stakeholders.

You will contribute to propensity and causal modelling, product recommendations and client relationship intelligence, while monitoring models in production and communicating results

Qualifications

• Master’s degree or PhD in a quantitative discipline (e.g., Data Science, Mathematics, Statistics, Econometrics, Computer Science, Physics or Engineering).
• Master’s-level project, placement or internship experience, or ~1 year of relevant experience in data science.
• Experience applying statistical analysis, ML or data science techniques to practical problems.
• Strong understanding of mathematics, statistics, experimental design and model evaluation.
• Experience developing, testing and interpreting statistical or machine learning models.

Responsibilities

• Develop robust statistical models, ML solutions and data-driven tools.
• Prepare new data sources, assess data quality and create features for modelling.
• Apply propensity modelling, causal inference and experimentation to understand customer behaviour.

Skills

Statistical modelling

Machine learning

Python programming

SQL

Data science

Propensity modelling

Education

Master’s or PhD in a quantitative field

Tools

Python

SQL

Pandas

PySpark

Job description

Burberry is seeking an early-career Data Scientist to build scalable models and analytics across customer touchpoints. You will apply statistical modelling, machine learning and emerging AI techniques to meaningful business challenges, collaborating with data scientists, engineers and cross-functional stakeholders.

You will contribute to propensity and causal modelling, product recommendations and client relationship intelligence, while monitoring models in production and communicating results