R Team Lead

Key Responsibilities

• Spot-check R programming and data-analysis items.
• Review AI-generated R code, statistical explanations, visualizations, data-wrangling steps, and modeling workflows.
• Communicate project updates and R-specific standards on Discord.
• Answer trainer/QA questions around R syntax, packages, statistical reasoning, reproducibility, plots, and rubric interpretation.
• DM inactive contributors and manage activation tracking.
• Create and maintain R documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
• Run onboarding/training calls for R contributors.
• Flag misleading statistical claims, invalid methods, non-reproducible workflows, or hallucinated packages/functions.
• Improve QA processes based on recurring gaps.

Requirements

• Strong grasp of English to follow guidelines and provide clear feedback.
• 3+ years of experience using R for data analysis, statistics, research, analytics, teaching, coding, or technical review.
• Strong understanding of R syntax, data frames, vectors, functions, lists, factors, missing data, tidyverse, base R, statistical modeling, and visualization.
• Ability to identify issues such as incorrect statistical assumptions, invalid package usage, non-reproducible code, data leakage, flawed transformations, hallucinated functions, or misleading charts.
• Familiarity with dplyr, tidyr, ggplot2, readr, stringr, purrr, data.table, Shiny, R Markdown/Quarto, caret/tidymodels, lme4, survival, Git, and reproducible workflows is preferred.
• Experience leading remote teams of trainers, analysts, reviewers, educators, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and PM systems.
• Highly organized and able to maintain style guides, trackers, FAQs, honeypots, calibration tasks, and onboarding materials.
• Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based review is a strong plus.