R Engineer – Remote Contract – Up to $55/hr

About the role

We are looking for an experienced R Engineer to join our data‑science team on a contract basis. The role is fully remote and focuses on reviewing, improving, and generating high‑quality R code and statistical workflows produced by AI systems.

Key responsibilities

• Review AI‑generated R scripts, statistical analyses, and data‑science pipelines for correctness, reproducibility, and methodological soundness.
• Identify and correct errors in statistical methodology, data‑wrangling logic, modeling assumptions, and analytical interpretation.
• Evaluate R implementations for efficiency, readability, proper package usage, and adherence to best practices.
• Generate reference solutions, explanatory documentation, and reusable R workflows.
• Rank multiple AI‑generated responses based on analytical rigor, coding quality, and statistical validity.
• Apply reproducible research principles, including thorough data documentation and transparent validation procedures.

Required profile

• Bachelor’s degree or higher in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
• Strong professional experience (several years) using R for statistical analysis, data cleaning, modeling, and visualization.
• Solid understanding of applied statistics, regression, hypothesis testing, model validation, and inference.
• Excellent written English (C1 level) with the ability to explain complex statistical concepts clearly.
• Experience working with AI or large language models for coding assistance or data‑training workflows is strongly preferred.

Required skills

• R programming
• tidyverse ecosystem
• data.table package
• ggplot2 for visualization
• Machine‑learning libraries (e.g., caret, randomForest)
• Time‑series analysis libraries
• Statistical modeling frameworks
• Reproducible research principles and workflow documentation

What we offer

• Fully remote work arrangement
• Competitive hourly rate up to $55 USD
• Opportunity to influence AI‑driven data‑science tooling