Data Scientist
Role Overview
The Data Scientist role at Mercor involves conducting in-depth failure analysis on AI agent performance in finance-related tasks. The hire will work closely with data labeling experts and technical teams to identify patterns and root causes of performance issues, ultimately recommending improvements to enhance evaluation frameworks. This position is suited for mid-level professionals with a strong statistical background and familiarity with AI/ML concepts.
Perks & Benefits
Mercor offers a remote work setup, allowing flexibility in work hours while focusing on results. Team culture emphasizes collaboration, innovation, and continuous learning, with opportunities for career growth in the evolving finance tech landscape. Employees are encouraged to develop their skills in a supportive environment, working across various finance sub-domains and leveraging advanced data analysis tools.
Full Job Description
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Role Description
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.).
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
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
Requirements
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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