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A prominent online retailer in London is looking for a Supply Chain Data Scientist to enhance data-driven insights across various operations. The role involves analyzing data, developing models, and creating Value Insight Packages (VIPs) to identify process improvements and efficiencies. Ideal candidates should have a strong background in RPA, BI tools, and programming languages such as Python or SQL, along with excellent communication skills. The position encourages teamwork, with a hybrid work model.
ASOS Supply Chain have stood up a centralised data science team to provide value across all the facets of Supply Chain such as improving the success mix around last mile customer journey, driving accuracy in warehousing, optimised inbound routing and consolidating our returns operations. You will be responsible for identifying and demonstrating the strategic business value that process intelligence provides by holding teams to account using both data analytics and situational anecdotes. You are your teams trusted advisor to help them achieve their objectives, providing rigour and removing opportunities for bias. You will be part of a team that provides value; responsible for analysing large data sets, creating models, identifying themes and collating the data into Value Insight Packages (VIPs) which, subsequently drive mean shift improvements and / or remove variation from our processes.
We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That’s why our approach to working together includes spending at least 3 days a week in the office. It’s a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.