Mercor is 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.).
Requirements
- Strong foundation in statistical analysis, hypothesis testing, and pattern recognition
- Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis
- Experience with exploratory data analysis and creating actionable insights from complex datasets
- Understanding of LLM evaluation methods and quality metrics
- Comfortable working with Excel, data visualization tools (Tableau/Looker), and SQL
- Experience with AI/ML model evaluation or quality assurance
- Experience with multi-dimensional failure analysis
Responsibilities
- Identify patterns in AI agent failures across task components (prompts, rubrics, templates, file types, tags)
- Determine whether failures stem from task design, rubric clarity, file complexity, or agent limitations
- Analyze performance variations across finance sub-domains, file types, and task categories
- Create dashboards and reports highlighting failure clusters, edge cases, and improvement opportunities
- Recommend improvements to task design, rubric structure, and evaluation criteria based on statistical findings
- Present insights to data labeling experts and technical teams
Other
- Background in finance or willingness to learn finance domain concepts
- 2-4 years of relevant experience
