Data Scientist (Train AI Models Part Time!)

hackajob is collaborating with Mercor to connect them with exceptional tech professionals for this role. 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 We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.