Define and evolve Glean’s growth measurement framework across acquisition, activation, engagement, retention, resurrection, and expansion, including metrics such as WAU, activation, engagement intensity, retention, and feature adoption
Build and analyze end-to-end user and account growth funnels to understand where users experience value, where they drop off, and which behaviors are most predictive of durable engagement
Diagnose adoption gaps and develop bottoms-up growth strategies for high-impact enterprise accounts, identifying where product, deployment, engagement, or organizational barriers are limiting growth and partnering with Applied AI and R&D leaders on targeted interventions
Identify and size high-leverage growth opportunities across onboarding, product discoverability, education, lifecycle messaging, collaboration and virality, and new product surfaces
Partner across R&D and Applied AI to turn product capabilities and behavioral insights into scalable adoption plays, identifying the customers and user populations best suited for new experiences and translating those opportunities into targeted field interventions
Partner closely with Product, Design, and Engineering to translate product ideas into testable hypotheses, well-defined success metrics, instrumentation plans, and decision criteria
Design and analyze rigorous A/B tests, phased rollouts, and quasi-experiments; use causal evidence to recommend whether products should launch, iterate, or change direction
Develop behavioral and needs-based user segments and translate those insights into targeted product interventions
Inform roadmap and investment decisions by quantifying reachable populations, expected impact, confidence, dependencies, and tradeoffs before significant development begins
Build trusted, reusable growth datasets, dashboards, and self-serve analytical tools that allow Product and Engineering partners to independently understand product health and investigate changes
Lead cross-functional data science projects end-to-end, translating ambiguous product questions into clear insights, recommendations, and decisions for audiences ranging from engineers to executives
Example areas of focus could include improving new-user onboarding and activation, converting occasional users into habitual users, increasing adoption of emerging AI experiences, optimizing high-traffic entry surfaces, improving feature discovery, developing lifecycle strategies, and building account-level adoption frameworks for enterprise customers, and translating new product capabilities into scalable Applied AI adoption motions