• Own the Identity & Intelligence Platform roadmap, including device intelligence, identity risk signals, session integrity, anomaly detection, and automation or bot classification. • Define how first-party intelligence signals are collected, normalized, evaluated, and reused across products and workflows. • Partner with Engineering and Data Science to ensure intelligence signals and models are production-ready, reliable, and aligned with product outcomes. • Collaborate with Engineering on architectural decisions that balance accuracy, latency, scalability, and cost. • Own the cost efficiency of identity and intelligence signals, including data enrichment spend, infrastructure usage, and long-term unit economics of decisioning. • Make explicit tradeoffs between signal lift, latency, scalability, and cost, driving decisions that reduce unnecessary reliance and operational spend. • Own the strategy for external data enrichment, including when to buy, build, replace, or retire third-party identity and risk signals. • Extend identity intelligence into bot and automation detection, leveraging existing device, session, and behavioral signals. • Work with Risk, Customer Success, Sales, and Product Marketing to translate platform capabilities into clear value narratives and deployable solutions. • Stay current on evolving fraud, bot, and identity attack patterns, and ensure the platform adapts accordingly. • Operate with autonomy and urgency, making sound product tradeoffs in complex, cross-functional environments.