He spent a decade buying data to beat the market. Then he decided the real problem was that nobody could find it.
Founder & CEO — Nomad Data | New York
Type a question into Nomad Data the way you would ask a colleague — not in the stilted vocabulary of a database, but in plain, impatient English — and the platform goes looking. It hunts through documents you already own and vendors you have never heard of, then comes back with something most data tools forget to deliver: an answer. That is the whole bet. Brad Schneider, the company's founder and CEO, made it after watching the data industry spend years optimizing everything except the part where a human actually finds what they need.
Founded in 2020 and headquartered in New York, Nomad Data now sells to consultancies, insurers, hedge funds, private equity firms and corporate teams. Its flagship engine, Doc Chat, reads through thousands of unstructured documents and pulls out insights at a claimed 99% recall and precision, cutting document turnaround by roughly three-quarters. Around it sit a Data Relationship Manager for tracking who supplies what, and Connect for wiring in external sources. The pitch is less "buy our dataset" and more "stop hunting."
Schneider is an engineer who got detoured into finance for a decade, made good money there, and came back to engineering with a grudge against friction. He calls himself "an engineer by heart" and means it as a confession more than a credential.
People don't want data, they want answers.— Brad Schneider, on the sentence Nomad Data is built around
Schneider runs his days on a deliberately boring loop. The same meals. The same clothing. Not out of indifference, but as engineering applied to a human being: every trivial choice he removes is a choice he gets to keep for the work that matters. It is a quiet refusal to let his attention be nickel-and-dimed by breakfast.
The reading list is where it gets less predictable. He moves through genetics, history and computer science — not as hobbies but as a method. Cross-disciplinary knowledge, he has argued, is how you spot the pattern everyone staring at a single field will miss. An analyst sees a number. Schneider would rather see the shape the number is part of.
It is a strangely consistent personality for someone in a hype-prone industry: a man who minimizes his own decisions while building a machine to expand everyone else's.
Data should be easy to find, easy to purchase, and easy to integrate.— The complaint that became a company
Schneider holds a B.S. in Electrical Engineering and Computer Science from MIT and is a CFA charterholder — a rare pairing of someone who can write the code and read the balance sheet.
Nomad Data's promise is plain: connect a team's internal knowledge with the external data it is missing, and turn the gap into an answer — fast.
Extracts insights from unstructured text across thousands of documents — at a claimed 99% recall and precision, with ~75% faster turnaround.
Tracks every data interaction and relationship, so a team actually knows what it buys, from whom, and whether it's still worth it.
Integrates external data sources into the workflow, so discovery doesn't dead-end at "now go build the pipeline yourself."
Ask Schneider about the big-name data marketplaces and he gets pointed. The platforms everyone names — the cloud giants with "marketplace" in their menus — are, in his telling, compute companies wearing a marketplace costume. They make their money on storage and processing, so a genuine, frictionless data transaction was never really the goal. Formats stay narrow. The audience stays technical. The buyer who just wanted an answer stays stuck.
His other diagnosis is about the sellers. Plenty of companies sit on valuable proprietary data and have no idea it is valuable, or how to package it. The market is multidisciplinary by nature — numbers without context are just noise — which is exactly why keyword search fails. The words a buyer uses rarely match the words a vendor used. Language models, Schneider argues, finally bridge that gap.
Watch: Brad Schneider on InsurTechTalk, breaking down why finding data is harder than buying it.
Schneider's near-term worry about AI isn't runaway superintelligence — it's deepfakes, social engineering, and a world where you can't verify what you're looking at.
He points believers and skeptics alike to Erik Larson's The Myth of Artificial Intelligence. A founder in AI recommending a book that pumps the brakes — that's the tell.
An MIT engineering degree and a CFA charter in the same résumé. He can write the model and read the 10-K, which is most of why Nomad Data exists.
Data, like a nomad, should be free to roam wherever it is needed — never trapped behind fifteen clicks or a format nobody asked for.
Turn natural-language questions into answers for every business user, and retire the manual document slog for good.