Role overview
We're seeking a data-driven analyst to conduct comprehensive failure analysis on AI agent performance across finance-sector tasks. You'll identify patterns, root causes, and systemic issues in our evaluation framework by analyzing task performance across multiple dimensions (task types, file types, criteria, etc.).
What you'll work on
- Statistical Failure Analysis: Identify patterns in AI agent failures across task components (prompts, rubrics, templates, file types, tags)
- Root Cause Analysis: Determine whether failures stem from task design, rubric clarity, file complexity, or agent limitations
- Dimension Analysis: Analyze performance variations across finance sub-domains, file types, and task categories
- Reporting & Visualization: Create dashboards and reports highlighting failure clusters, edge cases, and improvement opportunities
- Quality Framework: Recommend improvements to task design, rubric structure, and evaluation criteria based on statistical findings
- Stakeholder Communication: Present insights to data labeling experts and technical teams
What we're looking for
- Experience with AI/ML model evaluation or quality assurance
- Background in finance or willingness to learn finance domain concepts
- Experience with multi-dimensional failure analysis
- Familiarity with benchmark datasets and evaluation frameworks
- 2-4 years of relevant experience
We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Tags & focus areas
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