No Priors Ep. 50 | With Stripe Head of Information Emily Glassberg Sands

(instrumental music) . Today, Sara and I are joined by Emily Glassberg Sands, who's the head of information at Stripe, which includes data science, growth, machine learning infra, business applications, and corporate technology. Emily was previously the VP of data science at Coursera, where she led development of AI-powered products to have personalized learning, scalable teaching, skill measurement, and more. We're excited to talk with Emily today about Stripe, AI, fintech, and education. Emily, welcome to New Priors.

Thanks so much for having me.

Oh, yeah. Thanks so much for joining. So you now lead the information org at Stripe. Can you tell us a little bit more about what the organization does, how it's evolved under your tenure, and what are some of the span of responsibilities that you're focused on?

Yeah, so I joined Stripe, uh, back in 2021, originally actually to lead data science. And David Singleton, Stripe's CTO, reached out. I didn't know a ton about Stripe, but I knew millions of businesses were using it to collect payments, which had to mean really interesting data on those businesses and on a large swath of the economy. Stripe's clearly helping companies run more effectively and also in a position to learn from its data what kind of interventions significantly improve companies' long-term success, uh, and in some cases, to actually action those. Today, I wear two hats. So, uh, the first is I support a bunch of different teams that are together tasked with enabling the effective use of data across Stripe, and this includes, you know, from decision-making internally to, uh, building data-powered products. We've been investing a bunch in foundations, which includes building out our ML infrastructure and better organizing our data, you know, the really sexy stuff. Um, but also in applications like seeding a bunch of new gen AI bets and, and getting them out to our users. So that's kind hat one, and then second, I'm accountable for our self-serve business. So a huge number of SMBs and startups come to Stripe directly. Um, to get started, they self-serve through the website, and we're really focused on understanding who those users are, um, getting them the right shape of integration efficiently, um, building product experiences, um, that meet their needs, including as they grow, um, and growing the portfolio of products they use. So, um, for many of our users, it's not just payments, but invoicing or subscriptions or billing or tax or rev rec, um, depending on, on what their business model demands.

Yeah, and I guess Stripe for a long time has been doing different things in ML in terms of traditional ML. You know-

Yeah.

... I think fraud detection and the fraud detection API that you all have is one example of that. But you were actually quite early in terms of adopting LLMs and sort of early generative AI models. Could you tell us a little bit more about how that came about, how the interest was sparked, and how adoption really took off?