Jobs
Benefits & Perks
•Healthcare
•401(k)
•Equity
•Home Office Stipend
•Learning Budget
•Free Meals
•Healthcare
•401k
•Equity
•Home Office
•Learning
•Meals
Required Skills
Python
SQL
Statistics
Machine Learning
About Glean:
Founded in 2019, Glean is an innovative AI-powered knowledge management platform designed to help organizations quickly find, organize, and share information across their teams. By integrating seamlessly with tools like Google Drive, Slack, and Microsoft Teams, Glean ensures employees can access the right knowledge at the right time, boosting productivity and collaboration. The company’s cutting-edge AI technology simplifies knowledge discovery, making it faster and more efficient for teams to leverage their collective intelligence.
Glean was born from Founder & CEO Arvind Jain’s deep understanding of the challenges employees face in finding and understanding information at work. Seeing firsthand how fragmented knowledge and sprawling SaaS tools made it difficult to stay productive, he set out to build a better way - an AI-powered enterprise search platform that helps people quickly and intuitively access the information they need. Since then, Glean has evolved into the leading Work AI platform, combining enterprise-grade search, an AI assistant, and powerful application- and agent-building capabilities to fundamentally redefine how employees work.
About the Role:
Glean is building a world-class Data Organization composed of product data science, applied science, data engineering and business intelligence groups. This is an applied science role based in our Palo Alto or San Francisco office. It covers a subset of:
A/B Experimentation:
-
Collaborate with product data science and engineering teams to identify techniques, tooling and process improvements in online A/B experimentation to assist rigorous decision making across all relevant product domains.
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Develop and maintain our A/B experimentation platform based on stakeholder feedback
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Write code or identify vendors to deploy these techniques to production in a scalable manner that’s easy to use by engineering, product data science, product management and design teams
Generative AI evaluation:
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End to end evaluation of various hero use cases like document/URL uploads, including evaluation set generation, coming up with evaluation criteria and methods to interpret the results.
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Breaking down end to end evaluations into more granular evaluation of various tasks & skills including but not limited to content summarization/analysis/generation, multi-step reasoning & strategizing, tool selection & use, coding & system design.
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Design, development and ownership of best practices, tools and processes across various evaluation problems, e.g. query intent classification, standardizing the use of best statistical principles to handle LLM stochasticity, industry benchmarking.
About you:
-
You have 5+ years of experience as a Masters degree holder, 3+ as a PhD degree holders (Masters/PhD degree in Statistics, Mathematics or Computer Science, or another quantitative field)
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You’re strong in statistics and/or machine learning. You have experience in applying these skills into tangible improvements in products, internal tools, and processes in a pragmatic way that puts business urgencies first.
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You are very proficient in Python, e.g. proficient enough to maintain an internal source-controlled library used by dozens of others.
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You are concise and precise in written and verbal communication. Technical documentation is your strong suit.
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You are proficient in SQL and the modern data stack (e.g. source-controlled dbt pipelines for ETL/ELT).
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You are strong at defining good product KPIs/guardrail metrics, dashboarding and analysis of raw data to derive strategic insights.
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You have experience in B2B SaaS.
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You have experience working on ranking, developing, and maintaining A/B experimentation platforms and/or ML measurement problems.
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You are passionate about using AI to improve the productivity of data teams as well as non-data professionals trying to derive more value of their company’s data.
Location:
- This role is hybrid (3-4 days a week in one of our SF Bay Area offices)
Compensation & Benefits:
The standard base salary range for this position is $175,000 - $230,000 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 are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
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About Glean

Glean
Series BA free, personalized, intelligent news feed that helps you find the best events, apps, and articles on your Android or iOS phone
1-50
Employees
New York
Headquarters
$2.2B
Valuation
Reviews
3.7
2 reviews
Work Life Balance
4.0
Culture
3.0
Career
4.0
Management
3.5
65%
Recommend to a Friend
Pros
Flexible hours and hybrid working
Strong commitment to Lean philosophy
Good learning opportunities
Cons
Too many meetings
Less favorable treatment for non-native English speakers
Language discrimination issues
Salary Ranges
38 data points
Junior/L3
Mid/L4
Senior/L5
Staff/L6
Junior/L3 · Data Scientist
0 reports
$261,300
total / year
Base
-
Stock
-
Bonus
-
$222,105
$300,495
Interview Experience
2 interviews
Difficulty
3.5
/ 5
Duration
14-28 weeks
Offer Rate
50%
Experience
Positive 50%
Neutral 0%
Negative 50%
Interview Process
1
Final Interview
2
Final EM Interview
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