greenhouse
AI Data Analyst
Glean
Bengaluru, IND
Mid · 3–5 years experience
Discovered 1 weeks ago
gitservicenowsql
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Key Skills for This Role
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- Run ongoing human labeling for priority AI workflows, including response quality, task success, MCP and tool use, 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.
- 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.
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