{"html":"<p>In this hourly, remote contractor role, you will work as an R Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across R programming and data-analysis AI training projects. You will review AI-generated R code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected standards.</p>\n<p>You will assess work for code correctness, statistical validity, data reasoning, package usage, reproducibility, debugging accuracy, readability, visualization quality, formatting, instruction-following, and rubric adherence.</p>\n<p>This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your R quality leadership will help ensure R training data is accurate, reproducible, statistically sound, clearly explained, and aligned with client expectations.</p>\n<p>Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.</p>\n<p>Important:\nThere is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.</p>\n<h3>Responsibilities</h3><ul><li>Spot-check R programming and data-analysis items.</li><li>Review AI-generated R code, statistical explanations, visualizations, data-wrangling steps, and modeling workflows.</li><li>Communicate project updates and R-specific standards on Discord.</li><li>Answer trainer/QA questions around R syntax, packages, statistical reasoning, reproducibility, plots, and rubric interpretation.</li><li>DM inactive contributors and manage activation tracking.</li><li>Create and maintain R documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.</li><li>Run onboarding/training calls for R contributors.</li><li>Flag misleading statistical claims, invalid methods, non-reproducible workflows, or hallucinated packages/functions.</li><li>Improve QA processes based on recurring gaps.</li></ul><h3>Requirements</h3><ul><li>Bachelor’s or Master’s degree in Statistics, Data Science, Mathematics, Computer Science, Economics, Biology, Social Sciences, or related quantitative field.</li><li>Strong grasp of English to follow guidelines and provide clear feedback.</li><li>3+ years of experience using R for data analysis, statistics, research, analytics, teaching, coding, or technical review.</li><li>Strong understanding of R syntax, data frames, vectors, functions, lists, factors, missing data, tidyverse, base R, statistical modeling, and visualization.</li><li>Ability to identify issues such as incorrect statistical assumptions, invalid package usage, non-reproducible code, data leakage, flawed transformations, hallucinated functions, or misleading charts.</li><li>Familiarity with dplyr, tidyr, ggplot2, readr, stringr, purrr, data.table, Shiny, R Markdown/Quarto, caret/tidymodels, lme4, survival, Git, and reproducible workflows is preferred.</li><li>Experience leading remote teams of trainers, analysts, reviewers, educators, or QAs is strongly preferred.</li><li>Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and PM systems.</li><li>Highly organized and able to maintain style guides, trackers, FAQs, honeypots, calibration tasks, and onboarding materials.</li><li>Experience with AI training, data annotation, LLM evaluation, code QA, or rubric-based review is a strong plus.</li></ul>"}