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2023 DataIQ 100

Meg Hoxha, senior director global AI for operations, Levi Strauss & Co

Describe your career to date

I have a background in statistics and, when data science started to take off in the early 2010s, I decided it was the job for me so I got a formal education in machine learning. After that, I started working as a data scientist first at HP and then at Levi Strauss. I became the analytics manager of a small team focused on merchandise planning and inventory management, and then I became the lead for data, analytics and AI for Levi Strauss in Europe. Since the beginning of 2022, I have been the lead for data, analytics and AI for operations globally.

 

My focus has always been on tangible business impact and integration with the business function, starting with models at HP that predicted partner sales and the next best offer for telesales agents, which became an integral part of the ways of working and led to increased revenue and efficiency. Then I worked on predicting demand for computer components for the supply chain, which made me fall in love with the complexity of operations and ultimately led me to Levi Strauss.

 

I am very proud of the impact that my team and I have had on the top and bottom line performance, with initiatives in promotions, pricing, various routing optimisations across many nodes of the supply chain and our breakthrough in computer vision driven assortments. Starting very small and working nimbly with end users has always been central to our success. Scalable growth is now my focus.

Meg Hoxha 2023.jpg

What stage has your organisation reached on its data maturity journey?

On a scale from basic, emerging, strategic, integrated and exponential, we’re at a strategic maturity level for AI, where we’re scaling initiatives globally, and between basic and emerging for other data activities, where we’ve built foundations, but they haven’t yet added incremental value.

 

Tell us about the data and analytics resources you are responsible for

I lead a team of 30 people across the US and Europe who work in cross-functional squads with product managers, project managers, ML engineers and feature engineers dedicated to our initiatives, as well as devOps, MLOps and dataOps teams working across projects. The team focuses on product assortment, demand forecasting, pricing and promotions, inventory management, product allocation and replenishment.

 

We have a fairly mature capability at this point, some people have been here for up to three years, and the ways of working and processes are very similar, if not the same, across squads.

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