Senior Data Scientist, Growth

Details

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 and focuses 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 will turn ambiguous growth opportunities into measurable product bets, build measurement and experimentation systems, and use behavioral data to identify where Glean can create more value for users.

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, 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.
• Build trusted, reusable growth datasets, dashboards, metrics, and self-service analytical tools.
• Lead cross-functional data science projects end-to-end, from ambiguous product questions to insights, recommendations, and decisions for technical and executive audiences.

Example focus areas 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.
• 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 product decisions.
• Strong proficiency in SQL and practical fluency in Python or R.
• Experience building durable analytical datasets, metrics, dashboards, and data models. dbt experience is a plus.
• Ability to partner with Product and Engineering teams to identify opportunities and influence roadmap decisions.
• High AI proficiency through habitual, high-value use of LLMs, with sound judgment, rigorous validation, and continuous workflow improvement.
• Strong product and business mindset, including experience defining KPIs, guardrail metrics, and measurement frameworks.
• Ability to independently own complex projects from problem framing and measurement through analysis, recommendation, and follow-through.
• Clear, concise communication skills for explaining complex quantitative findings to technical and non-technical audiences.
• Experience in B2B SaaS, especially enterprise AI, or with products adopted across both users and accounts, is particularly valuable.
• Experience identifying growth opportunities from behavioral data and turning them into shipped, measurable product improvements is particularly valuable.
• Experience building experimentation or product-measurement capabilities is particularly valuable.
• Strong product intuition, ownership, self-motivation, and ability to manage changing priorities.

Work Arrangement

This is a hybrid role requiring four days per week in Glean's Mountain View office.

Compensation And Benefits

The standard base salary range is $200,000–$260,000 annually. Compensation may also include variable compensation, equity, and benefits depending on factors such as location, level, job-related knowledge, skills, and experience.

Benefits include medical, vision, and dental coverage, generous time off, a 401(k) plan, a home office improvement stipend, annual education and wellness stipends, regular company events, and healthy lunches daily.

Glean is committed to building and sustaining a diverse, inclusive workplace.

Interview Process

Candidates complete a brief AI-focused exercise or discussion about how they think about, design, and use AI to drive impact in their role.