Responsibilities

• Review AI-generated R code, statistical analyses, and data-science workflows for correctness, reasoning quality, reproducibility, and methodological accuracy
• Evaluate data-analysis solutions involving statistical modeling, regression, inference, machine learning, time-series analysis, data cleaning, and visualization in R
• Identify errors in statistical methodology, data-wrangling logic, modeling assumptions, analytical interpretation, and reproducibility workflows
• Analyze R implementations for correctness, efficiency, readability, package usage, and adherence to best practices in data science and statistical computing
• Generate high-quality reference solutions, analytical explanations, reusable R workflows, and structured statistical reasoning examples
• Compare and rank multiple AI-generated responses based on analytical soundness, coding quality, statistical validity, and clarity of reasoning
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