Applied Data Scientist

Senior Applied Data Scientist

London | Full-time | Hybrid

About dunnhumby

dunnhumby is the global leader in Customer Data Science, helping the world's most ambitious retailers and brands put the customer at the heart of every decision. With nearly 3,000 experts across Europe, Asia, Africa and the Americas, we partner with iconic businesses like Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro to turn data into growth, innovation and measurable value for their customers.

Applied Data Science

Our Applied Data Science team turns dunnhumby’s analytical capability into real commercial impact for our clients. Working directly with retailers and brands, we design and deliver the models, algorithms and insights that answer the hardest questions in retail — from personalisation and basket analysis to pricing, promotions and customer loyalty. Every insight we produce is grounded in real customer data and built to make a measurable difference.

About the Role

We’re looking for a Senior Applied Data Scientist who expects more from their career. This is an opportunity to apply your expertise to distil complex problems into meaningful, actionable insights -leveraging world-first science, cutting-edge machine learning techniques and human creativity to deliver impactful solutions for clients.

Joining our Retail Insight team, you’ll investigate, develop, implement and deploy a range of complex applications and components for our clients in Tesco, working alongside exceptionally talented colleagues who are challenging and rewriting the rules rather than simply following them.

We work collaboratively with our client leadership team and with our clients , using all of the tools and techniques available to us to deliver insight and build solutions that are re-usable, scalable and deliver measurable value.  You’ll drive change, shape innovative solutions, and actively redefine how organisations harness data.

What We’re Looking For

• Degree in Statistics, Mathematics, Physics, Economics, or a related quantitative field.
• Strong programming skills — Python and SQL are essential; PySpark experience is highly advantageous.
• Experience with version control tools (e.g. GIT).
• Solid understanding of analytical technologies, tools, and techniques.
• Excellent logical thinking and problem‑solving abilities.
• Strong communication skills, with the ability to explain complex concepts clearly to non‑technical audiences.
• Proven experience in statistical modelling and applying data science solutions to real client problems.