Job Description: AI Task Evaluation & Statistical Analysis Specialist
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.).
Key Responsibilities
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
Required Qualifications
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
Preferred Qualifications
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
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