The Work
Review AI-generated data science explanations, Python, R, and SQL code, modeling workflows, dashboards, experiment designs, and step-by-step reasoning. You will assess technical accuracy and give clear written feedback that improves work across projects.
You will also identify repeated quality issues, communicate guideline and workflow changes, support contributor onboarding and training calls, and maintain resources used by trainers and QAs. This role combines data science QA leadership with team lead responsibilities, so review the duties and qualifications before applying.
- Check statistics, model selection, code correctness, reproducibility, metric interpretation, business context, instruction following, and rubric adherence.
- Find data leakage, flawed assumptions, incorrect metrics, weak methods, non-reproducible code, and misleading conclusions.
- Review work involving regression, classification, clustering, experimentation, model evaluation, and validation methods.
- Create and maintain style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
- Communicate updates to trainers and QAs and help improve quality processes.
What It Pays And Takes
This is a remote, part-time contractor role open to candidates in the United States. The work requires strong English communication for technical feedback and team coordination.
- Pay: $110 per hour.
- Time: 20+ hours per week.
- Location: Open to candidates in the United States.
- Experience level: Entry level.
- Education: A degree in data science, statistics, computer science, machine learning, mathematics, economics, engineering, or a related quantitative field.
- Experience: 3+ years in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review, or data science education.
- Technical knowledge: Statistics, model evaluation, experimentation, regression, classification, clustering, and validation methods.
- Tools: Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud or data platforms.
- Helpful experience: AI training, data annotation, large language model evaluation, data science QA, or rubric-based technical review.
- Helpful background: Leading or supporting remote teams and using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- You should be highly organized and able to maintain documentation and quality resources.
How It Works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI Training Work
AI training is the human work behind systems that generate text, code, images, and other outputs. People review examples, rate model responses, and check technical quality so AI systems learn to produce more useful and accurate results.
Specialists are needed for work such as data science QA because strong subject knowledge helps identify errors that general reviewers may miss. OpenTrain helps people find AI training projects, build a profile, and apply in minutes.