Jayashree Dutta Verified listing

Jayashree Dutta learned Walmart's data systems from inside the building rather than from a Seller Center screen. As an Engineering Leader at Walmart Global Tech she led core data platform initiatives, including a real-time streaming platform handling more than 100 million events a day. She left to build a company around the problem that work exposed. That sequence is the whole reason a seller or supplier should take her call.

She got there through most of the hard data problems in consumer technology. At Robinhood she built the Risk Engineering and Data Science functions from nothing and shipped near real-time pipelines that cut margin calls by 16 percent. At Uber she led Customer Service Automation, Financial Products, and Identity and Access Management, and rebuilt the customer identity platform. Before that she ran the campaign management and ad insights platform at Netflix, owned the buyer pipeline at The RealReal, authentication through fulfillment, and spent four and a half years at Intuit leading QuickBooks.com.

The problem she left Walmart to solve is the one she had watched capable people solve by hand at company after company. Product data work a machine should be doing was consuming teams that should have been growing the business. What she built reflects an engineer's instinct about where AI belongs and where it does not, with generation and validation running as separate systems rather than one blended pipeline.

In 2025 she won She Pitches, She Wins at TiEcon, hosted by TiE Silicon Valley and powered by the Mayfield AI Garage, taking a $50,000 equity-free grant from Mayfield Fund and a place in the TiE Silicon Valley GTM Accelerator. Atronous was one of 50 companies selected by the NRF Innovation Advisory Committee for the 2026 NRF Innovators Showcase.

The reason to start a conversation with Jayashree is that she will tell you what her product does not do. She has spent a career on the buy-versus-build side of vendor evaluations and hiring the teams that inherit the answer, which tends to produce founders who are specific rather than promotional. If your team is carrying an item setup backlog, or your live SKU count is well short of what your catalog should support, she can tell you inside one conversation whether the constraint is your data or something else.