dunnhumbyis 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.
dunnhumbyemploys 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.
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.
You 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 focused on ensuring the right product is in the hands of the customer, stretching from how category needs vary across stores and online, identifying new and growing needs, optimizing the mix of products, predicting the impact of changes and maximizing the in-store experience.
What we expect from you
- Master's degree or equivalent in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field. A PhD in any of these fields is especially beneficial.
- Experience with machine learning techniques such as regularized 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, scikit-learn, along with data visualization technologies.
- Experience in the retail sector would be an added advantage.
A plus if you also have:
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Applied Statistics, Physics, Engineering, Biology or related field.
- Experience in the retail sector.
What you can expect from us
We won't just meet your expectations. We'll defy them. So you'll enjoy the comprehensive rewards package you'd expect from a leading technology company. But also, a degree of personal flexibility you might not expect. Plus, thoughtful perks, like flexible working hours and your birthday off.
You'll also benefit from an investment in cutting-edge technology that reflects our global ambition. But with a nimble, small-business feel that gives you the freedom to play, experiment and learn.
And we don't just talk about diversity and inclusion. We live it every day - with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our recruitment process. Please let us know how we can make this process work best for you. For an informal and confidential chat please contact to discuss how we can meet your needs.
Our approach to Flexible Working
At dunnhumby, we value and respect difference and are committed to building an inclusive culture by creating an environment where you can balance a successful career with your commitments and interests outside of work.
We believe that you will do your best at work if you have a work/life balance. Some roles lend themselves to flexible options more than others, so if this is important to you please raise this with your recruiter, as we are open to discussing agile working opportunities during the hiring process.
For further information about how we collect and use your personal information please see our Privacy Notice.
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