dunnhumby
AI

Senior Research Data Scientist

dunnhumby · London, ENG, GB

Actively hiring Posted about 4 hours ago

Role overview

dunnhumby is the global leader in Customer Data Science, partnering with the world's most ambitious retailers and brands to put the customer at the heart of every decision. We combine deep insight, advanced technology, and close collaboration to help our clients grow, innovate, and deliver measurable value for their customers.

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, Nestlé, Unilever and Metro.

We're looking for a talented Senior 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 and suppliers.

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 and will apply these techniques and algorithms to create dunnhumby science solutions that can be delivered across our clients and engineered into science modules.

This role will be focussed across insight automation and product assortment, with applications of science including identifying new and growing product needs, optimising the mix of products, predicting the impact of changes, product attribute generation and applications of generative AI for product support and insights.

What you'll work on

  • Master's degree or equivalent in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
  • Experience with machine learning techniques such as regularised regression, clustering or tree-based ensembles, and the ability to implement them through libraries.
  • Experience with programming, ideally Python, and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Hadoop / Spark / SQL
  • Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas and scikit-learn, along with data visualisation technologies.
  • A willingness to present your work to both technical and non-technical audience and to contribute to the wider data science community.

Tags & focus areas

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Machine Learning Data Science Ai