Data Operations Specialist

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.
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.
Glean is seeking a Data Science Operations Specialist - Intern to sit within Support Operations and combine analytical rigor with practical execution to improve processes, drive operational efficiency, and enable support engineers with data-driven insights and tools. You will learn how to convert support data into metrics, dashboards, and experiments that improve customer outcomes and team performance.
Analyze support workflows and ticket data (e.g., volume, handle time, SLA risk) to surface trends, bottlenecks, and improvement opportunities.
ticket volume, FRT/TTR, CSAT, NPS, CES) using spreadsheets or BI tools.
Partner with Support Ops to improve tool configurations and workflows (e.g., Zendesk categories/forms, Jira data links), documenting changes and outcomes.
Contribute to runbooks and internal knowledge base content to standardize best practices and ensure process compliance.
Prototype analytics/ML helpers for support use cases (e.g., Assist in maintaining trusted data pipelines from Zendesk/Jira and product telemetry (basic ETL hygiene, data QA checks).
How a modern AI support organization uses data, experimentation, and MLOps to drive measurable outcomes.
Practical skills in SQL, Python, and BI for operational analytics and dashboarding.
Best practices for Zendesk and Jira configurations that improve agent efficiency and customer experience.
Currently pursuing a Bachelor's or Master's in Computer Science, Data Science, Statistics, Engineering, or a related field.
Comfort with data analysis fundamentals; basic proficiency in spreadsheets and at least one of SQL or Python.
Experience with a BI platform (e.g., Sigma, Looker, Metabase, Tableau) or equivalent dashboarding tools.
Exposure to ETL/ELT concepts or orchestration tools (e.g., Hands-on projects using LLM/ML for text classification or tagging are a plus.
Languages: Python, SQL
BI & Analysis: Sigma, Looker, Metabase, Tableau, spreadsheets
Data: ETL/ELT concepts (e.g., This role is hybrid (4 days a week in our Bangalore office)

Compensation & Benefits
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience.
We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.