We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use
for each heading only if content exists. Must not repeat job title. Must not include Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting: It includes "About Glean" (company description) - that's not part of role? Might be considered "About the role"? Actually there is a separate "About The Role" heading. The "About Glean" is company overview; we could include under "About the role"? Probably not; we could treat as part of "About the role" but the instruction says use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly.
We have "About The Role" paragraph describing role context. That's suitable for "About the role" section.
Then "You Will" bullet list (actually sentences with line breaks) describing responsibilities. That's responsibilities.
Then "You Are" bullet list of qualifications/experience. That's qualifications/requirements/skills. We need to separate into Requirements, Qualifications, Skills maybe. But we can combine if content overlaps. The instruction: prefer few substantive sections over many thin ones. So we could have sections: About the role, Responsibilities, Requirements (including qualifications and skills), Benefits, Pay, Schedule.
We have location: hybrid (4 days a week in Mountain View office). That's schedule? Actually schedule is hybrid 4 days a week. So we can put Schedule section.
Compensation & Benefits: includes base salary range, variable compensation, equity, benefits, home office stipend, education and wellness stipends, healthy lunches, events, 401k, etc. So we can have Benefits section (including those) and Pay section (salary range). Or combine? The instruction: include a section only when posting has real specific content. It does have Pay (salary range) and Benefits (list). So we can separate.
We must not include boilerplate like EEO, AI-First Mindset, Global Data Privacy Notice, etc. Remove those.
Also remove "About Glean" company description? It's not part of role but could be considered "About the role"? Probably not needed. The instruction: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." The "About Glean" is not metadata; it's company overview. Might be considered part of "About the role"? But we already have "About The Role" section. The company overview could be omitted as not directly about role? However we must preserve ALL substantive information; do not invent or summarize away detail. The "About Glean" is substantive about the company; but is it required? The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. So we should keep it. Where to put? Could be a preceding paragraph before headings, maybe as a
before sections. Since we cannot add arbitrary headings not in list, we can just include it as a
before the first heading. That's acceptable.
Thus output:
About Glean ... (the paragraph)
then
About the role
... (the About The Role paragraph)
then
Responsibilities
then
Requirements
- ... each qualification/skill
then
Benefits
then
Pay
... salary range
then
Schedule
... hybrid 4 days a week in Mountain View office
We need to ensure we don't include filler like "commensurate with experience". Not present.
Now extract specifics.
About Glean paragraph: It's long. We'll copy it exactly as given (maybe trim trailing spaces). We'll keep as one
but could be long; that's fine.
About The Role paragraph: starts with "About The Role Glean is building a world-class data organization..." We'll copy that paragraph.
Responsibilities: The "You Will" list includes multiple sentences each starting with a verb. We'll turn each into
. Need to preserve all content. Let's list them:
- 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.
Note: Some sentences end with periods; we keep them.
Now Requirements/Qualifications/Skills: The "You Are" list includes many bullet-like sentences. We'll extract each.
List:
- 7+ years of experience in a highly quantitative data science, product analytics, or growth analytics role, with a degree in Statistics, Mathematics, Computer Science, or a related field
- Demonstrated experience partnering with Product and Engineering teams to identify opportunities and influence product roadmap decisions
- Strong grounding in statistics, experimentation, causal inference, statistical power, segmentation, funnel analysis, and retention analysis
- 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
- Experience designing and analyzing product experiments and communicating causal findings in a way that drives clear product decisions
- Strong proficiency in SQL and practical fluency in a statistical programming language such as Python or R
- Experience building durable analytical datasets, metrics, dashboards, and data models rather than relying primarily on ad hoc analysis. dbt experience is a plus.
- A strong product and business mindset, with 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
- You Are a Particularly Good Fit If You have experience in B2B SaaS, especially enterprise AI, or have worked on products where adoption occurs across both users and accounts
- You have experience partnering with GTM or Post-Sales teams and are comfortable using data to challenge assumptions, shape account strategy, and drive impact through action.
- You have a track record of identifying growth opportunities from behavioral data and turning them into shipped, measurable product interventions
- You have helped build experimentation or product-measurement capabilities that increased the velocity and quality of decision-making for an organization
- You combine strong quantitative rigor with product intuition and are comfortable making recommendations in highly ambiguous problem spaces
- You have a very strong sense of ownership and self-motivation. You are laser-focused on delivering business impact while growing as an individual along with Glean
- You are good at managing evolving priorities while successfully delivering core initiatives
Note: The "You Are a Particularly Good Fit If You..." is a heading inside list; we can treat as separate bullet.
We'll put all under Requirements section.
Benefits: From "Compensation & Benefits" paragraph: "We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused."
We should also note "Certain roles may be eligible for variable compensation, equity, and benefits." That's part of compensation but we already have Pay section. We'll include variable compensation and equity maybe in Benefits? But better to keep Pay section for base salary range; Benefits section for other benefits.
Thus Benefits list items:
- competitive compensation (maybe redundant with Pay)
- Medical, Vision, and Dental coverage
- generous time-off policy
- opportunity to contribute to your 401k plan
- home office improvement stipend
- annual education and wellness stipends
- vibrant company culture through regular events
- healthy lunches daily
We could also include "competitive compensation" but that's covered in Pay. Might be okay.
Pay: "The standard base salary range for this position is $200,000 - $260,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits."
We'll put that in Pay section as a .
Schedule: "Location: This role is hybrid (4 days a week in our Mountain View office)". We'll put Schedule section with that sentence.
Now ensure we don't include any boilerplate like "AI-First Mindset", "Global Data Privacy Notice", etc.
Now produce HTML fragment.
We need to wrap sections in appropriate tags. No or . Just the fragment.
Let's craft:
About Glean Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter