Glean
Publiée il y a un mois · San Francisco
Senior Data Scientist (Growth)
- San Francisco
Description du poste
This role sits within the Growth and Enterprise Readiness Data Science team, with a primary focus on accelerating user adoption, engagement, and sustained product usage. As a Growth Data Scientist, you will be the quantitative partner to Growth Product, Engineering, and Applied AI (Post Sales) You’ll turn ambiguous growth opportunities into measurable product bets, build the measurement and experimentation systems that allow us to learn quickly, and use behavioral data to identify where Glean can create substantially more value for its users 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 whethe
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