R Engineer Work SnapshotJob Type: ContractLocation: RemoteCompensation: Up to $30 per hourLevel: Middle to Senior Level Roles & ResponsibilitiesReview AI-generated R code, statistical analyses, and data-science workflows for correctness, reasoning quality, reproducibility, and methodological accuracyEvaluate data-analysis solutions involving statistical modeling, regression, inference, machine learning, time-series analysis, data cleaning, and visualization in RIdentify errors in statistical methodology, data-wrangling logic, modeling assumptions, analytical interpretation, and reproducibility workflowsAnalyze R implementations for correctness, efficiency, readability, package usage, and adherence to best practices in data science and statistical computingGenerate high-quality reference solutions, analytical explanations, reusable R workflows, and structured statistical reasoning examplesCompare and rank multiple AI-generated responses based on analytical soundness, coding quality, statistical validity, and clarity of reasoningFact-check statistical claims, analytical outputs, model interpretations, and data-science methodologies using evidence-based reasoningApply reproducible research principles including data documentation, workflow consistency, validation procedures, and transparent analytical reasoningWork with common R ecosystems including tidyverse, data.table, ggplot2, machine learning libraries, and statistical modeling frameworksSupport AI model improvement through annotation workflows, statistical evaluations, quality assurance reviews, and structured technical documentation RequirementsEducation: Bachelor s degree or higher in Statistics, Mathematics, Computer Science, Data Science, or a closely related quantitative fieldMinimum 2+ years of hands-on professional experience using R for statistics, data analysis, data science, or quantitative researchStrong proficiency in R programming including data wrangling, reusable function development, package usage, and analytical workflow designSolid understanding of applied statistics including regression, inference, hypothesis testing, model validation, and statistical interpretationExperience conducting end-to-end analyses involving data cleaning, exploratory analysis, modeling, visualization, and reporting in RFamiliarity with R ecosystems such as tidyverse, data.table, ggplot2, and machine learning or time-series analysis librariesStrong analytical thinking and ability to evaluate statistical methodology, assumptions, model performance, and analytical correctnessExcellent English writing and communication skills with Minimum C1 English proficiency requiredComfortable explaining complex statistical concepts, analytical reasoning, and coding corrections clearly in written formSignificant experience using AI systems or LLMs for coding assistance, analysis design, debugging, or code review strongly preferredPrevious experience with AI data training, annotation, model evaluation, or technical QA workflows is strongly preferred