R Quality Assurance Lead - Remote
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Key skills for this role
About the Role
YO IT Consulting seeks an R Quality Assurance Lead to oversee quality and trainer performance on R programming AI training projects. You will review AI-generated R code, provide feedback, and ensure adherence to quality standards.
Key Skills for This Role
Responsibilities
- Spot check R programming and data analysis items for quality
- Review AI generated R code, statistical explanations, visualizations, and modeling workflows
- Communicate project updates and R specific standards on Discord
- Answer trainer/QA questions about R syntax, packages, and statistical reasoning
- Create and maintain R documentation, style guides, and onboarding materials
- Flag misleading statistical claims, invalid methods, or non reproducible workflows
Requirements
- Bachelor's or Master's degree in Statistics, Data Science, Mathematics, Computer Science, or related quantitative field
- 3+ years of experience using R for data analysis, statistics, research, or technical review
- Strong understanding of R syntax, data frames, tidyverse, base R, statistical modeling, and visualization
- Ability to identify issues such as incorrect statistical assumptions, non reproducible code, or hallucinated functions
- Experience leading remote teams of trainers, analysts, or QAs strongly preferred
- Familiarity with dplyr, tidyr, ggplot2, Shiny, R Markdown, Git, and reproducible workflows preferred
Full Job Posting
About This Role
- In this hourly, remote contractor role, you will work as an R Quality Assurance Lead 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.
- This role is a fast growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation model labs.
Your Profile
- Bachelor’s or Master’s degree in Statistics, Data Science, Mathematics, Computer Science, Economics, Biology, Social Sciences, or related quantitative field.
- 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.
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.
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