Mercor - Data Scientist, application via RippleMatch at RippleMatch

This role is with Mercor. Mercor uses RippleMatch to find top talent.

Role Overview

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.).

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

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.