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About This Role
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, safer decisions at every level.
Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025\), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026\), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50\+ industries and 1,000\+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI\-fluent, and turning the superintelligent enterprise from concept into reality.
If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more \- deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company.
About the Role:
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.
About you:
- 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.
You are particularly a good fit if you:
- 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.
Location:
- This role is hybrid (4 days a week in our San Francisco office)
Compensation \& Benefits:
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 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’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
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AI\-First Mindset at Glean:
At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI\-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required.
Global Data Privacy Notice for Job Candidates and Applicants:
Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.
Salary Context
This $200K-$260K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Glean Technologies, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($230K) sits 19% above the category median. Disclosed range: $200K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Glean Technologies AI Hiring
Glean Technologies has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $205K - $260K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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