Data Scientist

2 weeks ago

Mumbai, Maharashtra, India Mercor Full time ₹ 80,00,000 - ₹ 2,00,00,000 per year

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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