About the role
About Crosby AI Crosby is an AI-first legal platform reimagining corporate legal services from the ground up. We are a team of technologists and legal experts who build proprietary technology and human-in-the-loop workflows that meaningfully improve how lawyers and machines work together — delivering speed, consistency, and quality across high-stakes work. Our systems review complex documents faster and with exceptional accuracy, combining advanced AI with structured legal expertise. Clients receive AI-powered redlines, commentary, and negotiation guidance within hours, at a predictable, volume-based price. Backed by Sequoia, Index Ventures, and Bain Capital Ventures, we're building the end-to-end contracting platform for the next generation of fast-growing companies.
The Team The Engineering team builds the core systems and data infrastructure that power Crosby’s AI-first legal platform. We work across machine learning, product, and backend systems to deliver intelligent, reliable solutions in high-stakes environments. We prioritize speed, ownership, and rigor — building systems that are both technically excellent and deeply aligned with real-world outcomes.
The Role As Head of Data at Crosby, you'll play a critical role in defining and leading our data strategy across the company. You’ll be responsible for building the systems, pipelines, and teams that power our AI models, product analytics, and decision-making infrastructure. You’ll work closely with engineering, product, and legal teams to ensure we are leveraging data as a core competitive advantage — from improving model performance to driving business insights. This is a highly strategic and hands-on role, requiring both technical depth and strong leadership.
Responsibilities Own data strategy: Define and execute Crosby’s data strategy across ML, analytics, and infrastructure. Build and scale data systems: Design and implement robust data pipelines, storage systems, and tooling to support both product and modeling needs. Enable AI performance: Partner closely with ML and engineering teams to improve data quality, labeling systems, and feedback loops that drive model performance. Drive analytics and insights: Establish metrics, dashboards, and analytical frameworks to support product, business, and operational decision-making. Lead and grow