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.
You will:
Analyze support workflows and ticket data (e.g., volume, handle time, SLA risk) to surface trends, bottlenecks, and improvement opportunities.
Build lightweight dashboards and reports for key metrics (e.g., 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., auto-tagging, intent classification, duplicate detection) with guidance from mentors.
Assist in maintaining trusted data pipelines from Zendesk/Jira and product telemetry (basic ETL hygiene, data QA checks).
Support forecasting exercises (ticket volumes, channel mix) and simple capacity models to inform staffing and scheduling.
Run small A/B tests and quasi-experiments across macros/forms and measure impact on FRT, TTR, CSAT, and deflection.
About you:
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.
How to structure runbooks, SLIs/SLOs, and KPI reviews to align teams on action.
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.
Clear, structured written communication; ability to document steps, decisions, and outcomes.
Curiosity, ownership, and strong attention to detail.
Familiarity with support systems (e.g., Zendesk, Jira) and customer support metrics (e.g., CSAT, NPS, FRT, TTR).
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., dbt, Airflow, Dagster).
Introductory statistics for experimentation, forecasting, or anomaly detection.
Hands-on projects using LLM/ML for text classification or tagging are a plus.
Languages: Python, SQL
BI & Analysis: Sigma, Looker, Metabase, Tableau, spreadsheets
Ops Stack: Zendesk, Jira
Data: ETL/ELT concepts (e.g., dbt, Airflow, Dagster)
Location:
This role is hybrid (4 days a week in our Bangalore office)