YO IT Consulting
R Quality Assurance Lead - Remote
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
R Quality Assurance Lead - Remote
R Quality Assurance Lead
Job Type: Contract Location: Remote
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.
Key responsibilities include:
- Reviewing AI-generated R code and trainer/QA work
- Evaluating output quality against project guidelines
- Providing precise written feedback
- Ensuring contributors follow expected standards
Your focus will be on assessing work for:
- Code correctness
- Statistical validity
- Data reasoning
- Package usage best practices
- Reproducibility
- Debugging accuracy
- Readability and visualisation quality
- Formatting compliance
- Rubric adherence
This role is with a fast-growing AI Data Services company, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your leadership in R quality assurance will help ensure training data is:
- Accurate
- Reproducible
- Statistically sound
- Clearly explained
- Aligned with client expectations
The selection process involves:
- An AI interview
- A domain-specific task
- An interview with a recruiter
Note: There is no immediate project for this role. However, if qualified, you will be among the first experts reached for relevant opportunities in the future. This candidacy also grants access to future projects through our expert network.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Your Profile
Requirements:
-
Bachelor’s or Master’s degree in:
- Statistics
- Data Science
- Mathematics
- Computer Science
- Economics
- Biology
- Social Sciences
- or a 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
- Technical review
-
Deep understanding of:
- R syntax
- Data frames, vectors, functions, lists, factors
- Missing data handling
- Tidyverse (best practices)
- Base R
- Statistical modeling
- Visualization
-
Ability to identify and flag issues including:
- incorrect statistical assumptions
- invalid package usage
- non-reproducible code
- data leakage
- flawed transformations
- hallucinated functions
- misleading charts
-
Preferred (but not essential) familiarity with:
dplyr,tidyr,ggplot2,readr,stringr,purrr,data.table- Shiny
- R Markdown/Quarto
caret/tidymodels,lme4,survival- Git
- Reproducible workflows
-
Experience leading remote teams of:
- Trainers
- Analysts
- Reviewers
- Educators
- QAs (strongly preferred)
Get help with your application
Your very own career expert that helps elevate your application to the next level.
-
Comfort with tools:
- Discord
- Google Sheets/Google Docs
- Various trackers and dashboards
- GitHub
- PM (Project Management) systems
-
Organizational skills:
- Maintaining style guides
- Managing trackers, FAQs, honeypots
- Calibration tasks
- Onboarding materials
-
Bonus experience in:
- AI training
- Data annotation
- LLM (Large Language Model) evaluation
- Code QA
- Rubric-based review
Key Responsibilities
-
Spot-check R programming and data-analysis items
-
Review:
- AI-generated R code
- Statistical explanations
- Visualizations
- Data-wrangling steps
- Modeling workflows
-
Communicate:
- Project updates
- R-specific standards (via Discord)
-
Answer queries from:
- Trainers/QA professionals on:
- R syntax, packages, statistical reasoning
- Reproducibility
- Plots and rubric interpretation
- Trainers/QA professionals on:
-
Engage with inactive contributors via direct messages (DM) and update activation tracking
-
Create/maintain:
- R documentation
- Style guides
- Examples, trackers, FAQs
- Honeypots
- Onboarding materials
-
Run:
- Onboarding/training calls for R contributors
-
Flag:
- Misleading statistical claims
- Invalid methods
- Non-reproducible workflows
- Hallucinated packages/functions
-
Improve QA processes based on recurrent gaps
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location