This role is with Mercor. Mercor uses RippleMatch to find top talent.
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.).
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
Statistical Expertise: Strong foundation in statistical analysis, hypothesis testing, and pattern recognition
Programming: Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis
Data Analysis: Experience with exploratory data analysis and creating actionable insights from complex datasets
AI/ML Familiarity: Understanding of LLM evaluation methods and quality metrics
Tools: Comfortable working with Excel, data visualization tools (Tableau/Looker), and SQL
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.