Research Data Scientist

dunnhumby is the global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy. We always put the Customer First.

Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail – one of the world’s most competitive markets, with a deluge of multi-dimensional data – dunnhumby today enables businesses all over the world, across industries, to be Customer First.

dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro.

We’re looking for a talented Research Data Scientist who expects more from their career. It’s a chance to extend and improve dunnhumby’s world class science capabilities. It’s an opportunity to work with a market-leading business to explore new opportunities for us and influence global retailers.

Joining our team, you’ll work with world class and passionate people to apply machine learning and statistical techniques to business problems. You’ll contribute to the research and implementation of new approaches to address complex problems and perform data analysis and model validation. You’ll have the opportunity to present results to a variety of internal stakeholders

What we expect from you 

• Master’s degree or equivalent in Computer Science, AI, ML, Statistics, Physics, Engineering, Biology, or a related field. PhD preferred.
• Programming experience, ideally in Python, and ability to handle large data volumes with modern processing tools (e.g., Hadoop, Spark, SQL).
• Experience building CI/CD pipelines is a plus.
• Experience with tools like Git for code management and collaboration.
• Experience building and maintaining highly available production systems on GCP, Azure, or AWS.
• Proficiency with machine learning techniques such as regularized regression, clustering, or tree-based ensembles, and implementing them via libraries.
• Familiarity with open-source software, including machine learning packages (e.g., Pandas, scikit-learn), deep learning frameworks (such as PyTorch or TensorFlow), and data visualisation tools.
• Adaptable and quick learner in a fast-paced environment, producing high-quality code.
• Strong communication skills, with a willingness to present work to both technical and non-technical audiences, and to contribute to the wider data science community.
• Demonstrated ability to break down complex problems and develop innovative, data-driven solutions.

A plus if you also have:

• Experience in retail sector.