R Engineer Work Snapshot

• Job Type: Contract
• Location: Remote
• Compensation: Up to $55 per hour
• Level: Middle to Senior Level

Roles & 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
• Fact-check statistical claims, analytical outputs, model interpretations, and data-science methodologies using evidence-based reasoning
• Apply reproducible research principles including data documentation, workflow consistency, validation procedures, and transparent analytical reasoning
• Work with common R ecosystems including tidyverse, data.table, ggplot2, machine learning libraries, and statistical modeling frameworks
• Support AI model improvement through annotation workflows, statistical evaluations, quality assurance reviews, and structured technical documentation

Requirements

• Education: Bachelor s degree or higher in Statistics, Mathematics, Computer Science, Data Science, or a closely related quantitative field
• Minimum 2+ years of hands-on professional experience using R for statistics, data analysis, data science, or quantitative research
• Strong proficiency in R programming including data wrangling, reusable function development, package usage, and analytical workflow design
• Solid understanding of applied statistics including regression, inference, hypothesis testing, model validation, and statistical interpretation
• Experience conducting end-to-end analyses involving data cleaning, exploratory analysis, modeling, visualization, and reporting in R
• Familiarity with R ecosystems such as tidyverse, data.table, ggplot2, and machine learning or time-series analysis libraries
• Strong analytical thinking and ability to evaluate statistical methodology, assumptions, model performance, and analytical correctness
• Excellent English writing and communication skills with Minimum C1 English proficiency required
• Comfortable explaining complex statistical concepts, analytical reasoning, and coding corrections clearly in written form
• Significant experience using AI systems or LLMs for coding assistance, analysis design, debugging, or code review strongly preferred
• Previous experience with AI data training, annotation, model evaluation, or technical QA workflows is strongly preferred