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  • Meg Hoxha, Senior Director Global AI For Operations, Levi Strauss & Co

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.

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.

What challenges do you see for data in the year ahead that will have an impact on your organisation and on the industry as a whole?

Our biggest challenges will be in the following areas:

 

– System changes and migrations; this is an industry-wide challenge, especially for legacy companies, and requires a lot of cross-functional coordination, clarity on business impact and setting expectations on productivity impact.

 

– Feature store development and adoption; as we scale, it’s very important that we don’t duplicate work and that we streamline pipelines, getting teams to embrace the feature store will be critical.

 

– Cloud strategy; we have a multi-cloud strategy but it is important that we streamline the interplay.

 

– Data governance; this is also an industry-wide issue, and is fundamental to deliver data products on a global scale.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function?

Our data strategy initially focused only on the data and artificial intelligence function to demonstrate tangible business value, but over the past year it has expanded more broadly to include business dashboards and other applications that both help operational teams and further refine the data for the data and AI teams.

 

Have you been able to fix the data foundations of your organisation, particularly with regard to data quality?

Improving data quality is an ongoing journey and there are several teams focused on this, especially when it comes to product master data and customer master data. In a global organisation like ours, data quality is very much linked to data lineage and versioning, so that’s something we’re working on.

Meg Hoxha
has been included in:
  • 100 Brands 2023 (EMEA)