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

You will be at the forefront of dunnhumby’s research data science team where you will be translating a complex business problem into a data science problem and solving that with scalable and state-of-the art AI algorithms. Joining our research data science team, you will be helping identify new opportunities within the Data Science space for future dunnhumby solutions. Learn from experts and grow your career in our organisation

What We Expect From You

Degree or equivalent in a statistical or mathematical subject.

• 2-5 Years of experience in Data Science
• Good understanding of statistics and mathematical methodologies used – especially forecasting, regression, linear models, time series, hypothesis tests and optimisation
• Ability to prototype solutions using Python and Spark to facilitate development and testing of algorithms on large data sets
• Good understanding of machine learning techniques, with applications to classification, prediction and clustering
• Ability to apply and extend experimental design methodology to conduct rigorous measurement of treatment effects
• Good working knowledge of databases, including SQL, relational and non-relational data models
• Experience of using following algorithm at scale: Ancova, linear models with regularization, clustering, random forests, xgboost
• Research the latest machine learning approaches
• Good grasp of Object-Oriented Programming
• Ability to quickly learn open-source statistics and machine learning packages – Pandas, SciPy, Scikit - learning, Tensor flow.
  
  

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 stephanie.winson@dunnhumby.com 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 which can be found (here)