The Work
You will evaluate AI-generated analytical reasoning, code, and model outputs for accuracy, clarity, sound methods, and correct quantitative claims. You will also create reference answers and detailed prompts that help improve AI systems.
The work covers data science and machine learning problems, including statistical reasoning, model choices, reliability, and possible bias.
- Review AI-generated analysis, code, and model outputs.
- Write step-by-step reference solutions and clear explanations for complex data problems.
- Fact-check quantitative claims and identify statistical or methodological errors.
- Compare and rate multiple AI responses based on correctness and reasoning quality.
- Create prompts and responses for AI training across varied topics.
- Test models for inaccuracies or bias and assess their reliability across use cases.
What It Pays and Takes
This is remote, part-time contractor work. The structured experience level is entry level, and the role also lists substantial professional data science experience as a requirement.
- Pay: $60 to $100 per hour.
- Time: 20 or more hours per week.
- Location: Open to candidates in Germany, India, and the United States.
- Language: C1 or higher English proficiency, with the ability to write clear quantitative explanations.
- Education: Bachelor's degree or higher in data science, computer science, statistics, mathematics, or a closely related quantitative field.
- Experience: 5 or more years as a data scientist or in a closely related analytical role.
- Technical skills: Strong Python for data analysis and machine learning, including pandas, NumPy, and scikit-learn.
- Knowledge: Statistics, experimental design, applied probability, and machine learning model development, evaluation, and deployment.
- Data skills: Advanced SQL and experience working with large, complex datasets.
- Communication: Experience with data visualization tools or dashboards and presenting findings to stakeholders.
- Work style: Careful, systematic, and focused on quality, reproducibility, and evaluating reasoning steps.
- Helpful background: AI data training, annotation, AI-generated content review, model evaluation, research-oriented data science, or machine learning with business or product datasets.
About AI Training Work
AI training is the human work behind systems that learn from examples, including reviewing model answers, checking code, and rating reasoning. People with strong data science experience are needed to judge technical accuracy and create reliable examples for AI systems.
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