Senior Data Scientist, Growth

Glean•Mountain View, CA

14h•$200,000 - $260,000•Hybrid

About The Position

Glean is building a world-class data organization spanning data science, applied science, data engineering, and business analytics. 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, Design, and Product Marketing. 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. You will: Define and evolve Glean’s growth measurement framework across acquisition, activation, engagement, retention, resurrection, and expansion. Own core metrics such as WAU, activation, engagement intensity, retention, and feature adoption. Build and analyze end-to-end user and account funnels to identify where users realize value, where they drop off, and which behaviors predict durable engagement. Identify and size high-leverage opportunities across onboarding, product discoverability, education, lifecycle messaging, collaboration and virality, and new product surfaces. Partner with Product, Design, and Engineering to turn product ideas into testable hypotheses, clear success metrics, instrumentation plans, and decision criteria. Design and analyze A/B tests, phased rollouts, and quasi-experiments. Apply causal inference to recommend whether products should launch, iterate, or change direction. Develop behavioral and needs-based segments and translate 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, metrics, and self-serve analytical tools so Product and Engineering can independently understand product health and investigate changes. Lead cross-functional data science projects end-to-end—from ambiguous product questions to clear insights, recommendations, and decisions for audiences ranging from engineers to executives. Example areas of focus 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.

Requirements

7+ years of experience in quantitative data science, product analytics, or growth analytics, plus a degree in Statistics, Mathematics, Computer Science, or a related field.
Strong grounding in statistics, experimentation, causal inference, statistical power, segmentation, funnel analysis, and retention analysis.
Demonstrated experience designing and analyzing product experiments and translating causal findings into clear product decisions.
Strong proficiency in SQL and practical fluency in Python or R.
Experience building durable analytical datasets, metrics, dashboards, and data models—not relying primarily on ad hoc analysis. dbt experience is a plus.
Demonstrated ability to partner with Product and Engineering teams to identify opportunities and influence roadmap decisions.
Exceptionally high AI proficiency through habitual, high-value use of LLMs, with sound judgment about when and how to apply them, rigorous validation, and continuous workflow improvement.
A strong product and business mindset, including experience defining KPIs, guardrail metrics, and measurement frameworks that influence decisions.
Ability to independently own complex projects end-to-end, from problem framing and measurement through analysis, recommendation, and follow-through.
Clear, concise communication skills, with the ability to explain complex quantitative findings to both technical and non-technical audiences.

Nice To Haves

Have experience in B2B SaaS, especially enterprise AI, or with products adopted across both users and accounts.
Have identified growth opportunities from behavioral data and turned them into shipped, measurable product improvements.
Have built experimentation or product-measurement capabilities that improved the speed and quality of organizational decision-making.
Combine quantitative rigor with strong product intuition and are comfortable making recommendations in ambiguous environments.
Bring strong ownership and self-motivation, with a focus on business impact and continuous growth.
Manage changing priorities while consistently delivering core initiatives.

Responsibilities

Define and evolve Glean’s growth measurement framework across acquisition, activation, engagement, retention, resurrection, and expansion. Own core metrics such as WAU, activation, engagement intensity, retention, and feature adoption.
Build and analyze end-to-end user and account funnels to identify where users realize value, where they drop off, and which behaviors predict durable engagement.
Identify and size high-leverage opportunities across onboarding, product discoverability, education, lifecycle messaging, collaboration and virality, and new product surfaces.
Partner with Product, Design, and Engineering to turn product ideas into testable hypotheses, clear success metrics, instrumentation plans, and decision criteria.
Design and analyze A/B tests, phased rollouts, and quasi-experiments. Apply causal inference to recommend whether products should launch, iterate, or change direction.
Develop behavioral and needs-based segments and translate 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, metrics, and self-serve analytical tools so Product and Engineering can independently understand product health and investigate changes.
Lead cross-functional data science projects end-to-end—from ambiguous product questions to clear insights, recommendations, and decisions for audiences ranging from engineers to executives.