About Stuut

Stuut is the AI coworker that runs order-to-cash for the world’s leading enterprises. Across order management, credit, collections, payments, cash application, disputes and deductions, Stuut does the work end to end and keeps going until the cash hits the bank.

Over 150 companies, spanning the mid-market to the Fortune 50, including Honeywell, ZoomInfo and PerkinElmer, use Stuut to unlock up to 40% more cash flow and cut DSO by 47%. More than $3 billion has moved through the platform to date.

Founded in New York by Tarek Alaruri and Ben Winter, Stuut has raised $93 million from Insight Partners, Andreessen Horowitz, M12, Microsoft’s Venture Fund, and others. Learn more at stuut.ai.

The Role

To build the data foundation that powers Stuut's intelligence layer. You'll work closely with our product and engineering teams to transform raw financial data into actionable insights that help our customers get paid faster. This is a foundational role, you'll be our first data hire, which means you'll shape everything from our data architecture to how we think about analytics.

This is a high-impact role for someone who can think strategically about data infrastructure while rolling up their sleeves to build pipelines, models, and systems from scratch. You'll translate messy data into clean, reliable datasets that drive product decisions, customer insights, and business growth. If you've ever wanted to own the entire data stack at a fast-growing company, this is it.

What You’ll Do

• Build and own our data infrastructure from the ground up — design pipelines that ingest, transform, and model data from customer ERPs, payment processors, and internal systems
• Build the transformation and semantic layer that serves as the single source of metric truth across customer-facing analytics, internal reporting, and our AI/ML systems
• Design the canonical data model that normalizes information across heterogeneous source systems, with quality tests and observability built in from day one
• Build the event and signal pipelines that turn product interactions and outcomes into clean, labeled data — the foundation for analytics, ML, and intelligent product features
• Partner with product, engineering, and applied ML to embed data quality, lineage, and observability into everything we ship
• Implement DataOps best practices so our data — and the AI features built on top of it — stays timely, accurate, and trusted