R Team Lead

SME Careers; United States; Remote; USD 55,000 - 96,000; Part time

Job summary

SME Careers, a fast-growing AI Data Services company, seeks an R Quality Assurance Lead (QAL) to oversee quality and trainer performance across R programming and data-analysis AI training projects. You will review AI-generated code, assess statistical validity, provide precise feedback, and ensure compliance with project guidelines.

You will lead QA reviews, communicate standards on Discord, and help maintain style guides, documentation, and onboarding materials for a global remote team.

Qualifications

• 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.
• 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.

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.

Skills

R programming

Statistics

Data analysis

English proficiency

Mentoring remote teams

Education

Bachelor’s or Master’s degree in Statistics/Data Science/Mathematics/CS

Quantitative field

Tools

dplyr

tidyr

ggplot2

readr

stringr

purrr

data.table

Shiny

R Markdown/Quarto

caret/tidymodels

lme4

survival

Git

Job description

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.

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.

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.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important:
There 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.

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

• 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.