AI Data Analyst

Overview

Glean is seeking an AI Data Analyst to run human evaluation of AI product quality, label failure modes, and maintain benchmark datasets that support response quality, competitive benchmarking, and release readiness.

What you'll do

• Run ongoing human labeling for priority AI workflows, including response quality, task success, MCP and tool use, and end-to-end Cowork-style workflows.
• Triage bad-query reports, downvotes, escalations, and routed quality issues; categorize failure modes and feed clean analysis back to partner teams.
• Perform qualitative analysis to identify recurring patterns such as hallucinations, retrieval failures, tool-use issues, weak grounding, and poor workflow completion.
• Follow and improve labeling guidelines and rubrics so judgments are consistent, realistic, and useful for model and product improvement.
• Maintain high-quality labeled datasets, golden sets, and regression slices; monitor coverage, drift, leakage, and difficulty distribution.
• Participate in calibration exercises with human graders and validate LLM-as-a-judge outputs against human labels to improve consistency and judge quality.
• Partner with engineers, PMs, QA, and eval owners on pre/post-change quality reads, benchmark updates, and release-readiness decisions.
• Contribute to recurring quality reporting by surfacing top failure modes, coverage gaps, quality shifts, and actionable recommendations.

What you'll need

• 3–5 years of experience in data labeling, data analysis, QA, or a related field.
• Strong analytical judgment and attention to detail, with the ability to apply nuanced rubrics consistently.
• Experience evaluating AI-generated outputs or working with NLP, search, recommendation, or other ML systems.
• Familiarity with structured qualitative analysis, basic SQL/data retrieval, and spreadsheet-based workflows.
• Clear written and verbal communication skills, with the ability to explain findings to both technical and non-technical audiences.
• Ability to work independently in ambiguous, fast-moving environments and collaborate effectively across functions.

Details

• Location: Bangalore, India.
• Hybrid: 4 days a week in the office.