Building a Data-Driven Marketplace for 600,000 Retailers at Faire

The speaker reflects on the importance of making the right decisions when building data infrastructure for machine learning and the need for the field of data science to adopt software engineering principles.

• Data infrastructure is sticky and difficult to move away from, so it's important to make the right decisions when building it
• The field of data science and machine learning can benefit from adopting software engineering principles
• Data scientists should be screened for coding skills and encouraged to code into the back-end code base
• Modular, testable, and simple systems are necessary for compounding interest in machine learning work
• The field of machine learning and AI has been heavily seeded from academia, which hasn't always used the best engineering practices

The speaker shares their experience of writing throwaway scripts in grad school to analyze data, which led to making the same mistakes over and over again. They learned from engineers at Square the importance of building testable, modular, and simple systems to allow for compounding interest in their work. They believe that the field of data science and machine learning can benefit from adopting software engineering principles to build modular systems that make everyone more productive and happier in their jobs.

Faire is an online marketplace that connects the world’s “best independent brands” with local retailers. There are over 85,000 brands and over 600,000 retailers using the platform across Europe and North America. Daniele Perito, Co-Founder and Chief Data Officer of Faire, will discuss how the marketplace is using machine learning to grow sales for both their small business customers and ultimately their consumers as well. Retailers on Faire can buy inventory and pay 60 days later, giving them the ability to return products that don’t sell, without disrupting their cash flow. Brands on Faire benefit from analytics and marketing tools that allow them to grow their customer base and their business. Offering these value propositions and acting as a matchmaker between brands and retailers requires dozens of models that span the gamut of machine learning and data techniques, from information retrieval to entity resolution to anti-fraud to default risk assessment with binary classifiers to operations research and econometrics. Learn how Faire built and curated tools and infrastructure to be able to run thousands of trials simultaneously and experiment at scale. Prior to Faire, Perito was Director of Security, Risk for Square Cash, where he worked on building secure, fast, and easy-to-use products.