AI and Data Science

Introducing: Data Science at Syrup

Mike Smith
Mike Smith
Jun 27, 2024
5 minutes to read

Hi there, I’m Mike, Head of Data Science at Syrup. If it’s not clear why an inventory optimization company would need a team full of data scientists, this is the post for you.

Let's start with the basics. My team and I are challenged to deliver the best forecasting and optimization engine available for brands and retailers, powered by AI.

But data science can’t operate in isolation, at least not if you want to have meaningful product and customer impact. We have purposefully set up our company so that data science forms its backbone, and likewise, AI is a core element of our very identity.

Why? It boils down to three key reasons that all tie back to our company mission: to create a world where commerce is no longer wasteful.

1. It ensures AI is a central element of our product.

It’s all too easy, especially these days, for AI to be a hooky but unoptimized add-on to existing software. In many cases, product vision and AI vision are separate things — the same basic product gets marketed with some flashy AI buzz, but the two strategies remain independent.

At Syrup, that product-AI disconnect is unthinkable. And it all comes down to delivering customer value.

Our models are tailor-made for the apparel and footwear industry, giving us an extra edge in accuracy and insight. This means that every tweak, every improvement we make to our forecasting engine is rooted in the unique challenges and intricacies of the industry.

But model tweaks in isolation won’t mean much for the omnichannel brands that trust us with their precious inventory. Our AI services form the very identity of our platform, and that’s where the large majority of our data science energy is focused. It is, after all, the foundation on which everything else sits.

We also have data scientists working across our engineering and product teams, and our data science chapter helps ensure we’re all marching to the same beat. This close cross-functional approach ensures that our data science efforts are always aligned with our product vision, eliminating any gap between theory and practice.

2. It allows us to push the boundaries of what AI can do — for each unique customer and at scale.

Demand signals available for analysis are exploding…but making use of those signals to accurately predict and act on demand increasingly requires the complex, multivariate approach that only AI models can support.

My team’s mission, in a sense, is to crack the code of how to make use of these signals — and then ensure our customers can simply and quickly make the most of that behind-the-scenes sleuthing.

The starting point is our experimentation roadmap. We’re exploring new methodologies, playing with cutting-edge models, and pushing the boundaries of what AI can do with fashion-specific data.

I’m excited to announce that we’ll be publishing the results of some of our experiments in a series we’re calling Always Innovating.

Check out the first edition of Always Innovating.

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