Machine Learning jobs in United Kingdom

This role will focus on improving decision-making in category management by enhancing data-driven understanding of products and categories. Responsibilities include optimizing space and range, increasing automation in decision-making, and ensuring the right products reach customers.

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Physics, Engineering, Biology, or related fields.
• 3-5 years of relevant experience.
• Experience with machine learning techniques such as regularized regression, clustering, or tree-based ensembles, and proficiency in implementing them using libraries.
• Proficiency in programming, ideally Python, and handling large data volumes with tools like Hadoop, Spark, or SQL.
• Experience with open-source machine learning packages (e.g., Pandas, scikit-learn) and data visualization technologies.
• Experience in the retail sector is advantageous.

dunnhumby is a global leader in Customer Data Science, empowering businesses to thrive in a data-driven economy. We prioritize the Customer First approach.

Our mission is to enable businesses to grow by becoming advocates for their Customers. With expertise in retail and access to extensive multi-dimensional data, dunnhumby helps organizations worldwide to be Customer First. We employ nearly 2,500 experts across Europe, Asia, Africa, and the Americas, working with iconic brands such as Tesco, Coca-Cola, and Procter & Gamble.

Joining our team means working with passionate professionals on applying machine learning and statistical techniques to solve complex business problems. You will contribute to research, develop new approaches, perform data analysis, and present results to stakeholders. Your work will create solutions that benefit our clients and can be integrated into our science modules.

We offer a comprehensive rewards package, flexible working hours, and a supportive environment that encourages experimentation and learning. Our commitment to diversity and inclusion is demonstrated through active networks and initiatives, fostering an inclusive culture where everyone can thrive.

We value work/life balance and are open to discussing flexible and agile working arrangements during the hiring process.