R Quality Assurance Lead (QAL)

Job Summary

As an R Quality Assurance Lead (QAL), you will oversee quality, consistency, and contributor performance across R programming, statistics, and data analysis AI training projects.

This role involves reviewing AI-generated R code, evaluating trainer and QA outputs, maintaining quality standards, and ensuring training data aligns with project requirements and client expectations.

Your work will directly contribute to improving advanced AI systems by ensuring R training content is statistically sound, reproducible, technically accurate, and clearly explained.

Key Responsibilities

Quality Monitoring

• Spot-check R programming and data-analysis tasks
• Identify recurring quality issues and statistical errors
• Provide actionable written feedback and escalate critical concerns

Code & Analysis Review

• Review and evaluate:

  • R code
  • Statistical explanations
  • Data wrangling workflows
  • Visualizations
  • Modeling pipelines
  • Analytical reports

• Assess work for:

  • Statistical validity
  • Reproducibility
  • Code correctness
  • Data reasoning
  • Visualization quality
  • Instruction adherence

Trainer & QA Communication

• Communicate updates regarding:
  • Guidelines
  • Workflow changes
  • Statistical standards
  • R-specific quality expectations

Contributor Support

• Answer questions involving:
  • R syntax
  • Tidyverse packages
  • Statistical reasoning
  • Data visualization
  • Reproducibility
  • Rubric interpretation

Activation Management

• Follow up with inactive contributors
• Track engagement and participation
• Report contributor availability concerns

Documentation & Onboarding

• Create and maintain:

  • Style guides
  • Documentation
  • FAQs
  • Examples
  • Calibration tasks
  • Onboarding materials

• Conduct onboarding and training sessions for contributors

Statistical & Quality Review

• Identify and flag:
  • Invalid statistical methods
  • Misleading conclusions
  • Data leakage
  • Non-reproducible workflows
  • Hallucinated packages or functions
  • Misleading visualizations

Process Improvement

• Improve QA workflows and review processes
• Identify recurring quality gaps and implement corrective actions

Required Qualifications

• Bachelor's or Master's degree in:

  • Statistics
  • Data Science
  • Mathematics
  • Computer Science
  • Economics
  • Biology
  • Social Sciences
  • Related quantitative fields

• Strong English communication skills

• Minimum 3 years of experience using R for:

  • Data analysis
  • Statistics
  • Research
  • Analytics
  • Teaching
  • Technical review

• Strong understanding of:

  • R syntax
  • Data frames
  • Vectors
  • Lists
  • Factors
  • Functions
  • Missing data handling
  • Base R
  • Tidyverse
  • Statistical modeling
  • Data visualization

• Ability to identify:

  • Incorrect statistical assumptions
  • Invalid package usage
  • Data leakage
  • Flawed transformations
  • Hallucinated functions
  • Non-reproducible code

Preferred Qualifications

• Experience with:

  • dplyr
  • tidyr
  • ggplot2
  • readr
  • stringr
  • purrr
  • data.table
  • Shiny
  • R Markdown
  • Quarto
  • caret
  • tidymodels
  • lme4
  • survival

• Familiarity with:

  • Git
  • GitHub
  • Reproducible workflows

• Experience leading:

  • Analysts
  • Educators
  • Reviewers
  • QA teams
  • Remote contributors

• Experience with:

  • AI training
  • Data annotation
  • LLM evaluation
  • Rubric-based review

Why Join

• Help improve leading AI systems through statistical and technical quality assurance
• Flexible remote schedule
• Weekly payments
• Referral rewards and community incentives
• Access to future opportunities through SME Careers' expert network

Selection Process

1. AI Interview
2. Domain-Specific Assessment
3. Recruiter Interview

Important Note

There is currently no active project for this role. Qualified candidates will be added to the expert network and contacted when relevant opportunities become available.