Staff Data Scientist - Product Analytics

Ironclad is the leading AI contracting platform that transforms agreements into assets. Contracts move faster, insights surface instantly, and agents push work forward, all with you in control. Whether you’re buying or selling, Ironclad unifies the entire process on one intelligent platform, providing leaders with the visibility they need to stay one step ahead. That’s why the world’s most transformative organizations, from Rivian to the World Health Organization and the Associated Press, trust Ironclad to accelerate their business. We’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com or follow us on LinkedIn. This is a hybrid role. Office attendance is required at least twice a week on Tuesdays and Thursdays for collaboration and connection. There may be additional in-office days for team or company events. About the Role As a Staff Product Analytics Data Scientist, you will be the analytical backbone of how Ironclad understands, measu [... source excerpt omitted ...] and maintain our own dbt pipelines, mine large and messy datasets for signal, and partner closely with Product and Engineering to ship data products that put insight directly into customers' and teammates' hands. You'll wear a product analytics hat most days, an analytics engineering hat when the pipeline needs it, and a data science hat when a problem calls for experimentation, causal inference, or modeling. As a Staff-level individual contributor, you'll set analytical direction, raise the technical bar across the team, and influence senior stakeholders without formal authority. What You'll Do Own product understanding. Define, instrument, and analyze the metrics that describ [... source excerpt omitted ...] ts. Partner with Product and Engineering to turn analysis into shipped features—embedded analytics, benchmarks, insights, and AI-powered experiences—that deliver value directly to customers and internal teams. Wear the analytics engineering hat. Own and extend the dbt models and transformations that power your work. Because our team owns its pipelines, you'll design scalable, well-documented, well-tested data models and uphold consistent definitions across the warehouse and BI layer. Architect AI-ready data. Leverage AI to accelerate your own pipeline and analysis work, and structure our data assets, documentation, and metadata so that both humans and LLMs can navigate them wit