Applied Scientist AI Model Evaluation

You will evaluate AI-generated responses involving statistics and machine learning, then create expert prompts, datasets, and reference solutions. Your feedback will identify flawed methods, statistical errors, and weak quantitative reasoning.

The role also includes applied data science work with complex datasets, experiments, machine learning models, and clear technical reporting.

• Design and run statistical analyses on complex datasets.
• Develop, tune, validate, and evaluate machine learning models.
• Use Python, Pandas, and scikit-learn for data modeling and large-scale data transformations.
• Define experimentation frameworks, analyze results, and document methods, findings, and recommendations.
• Review peer contributions and provide structured feedback.
• Deliver reproducible code and organized analytical reports.
• Evaluate AI-generated answers involving statistics and machine learning.
• Create prompts, datasets, and reference solutions for model evaluation.

This is a remote contractor role with part-time work available. The position is listed as entry level, and strong practical skills in statistics, machine learning, experimentation, and quantitative reasoning are central to the work.

• Pay: $245 to $280 per hour.
• Work type: Remote contractor and part-time.
• Language: Fluent in English.
• Location: Open to candidates in Algeria, Argentina, Austria, Bahrain, Bangladesh, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, France, Germany, Ghana, Greece, India, Indonesia, Italy, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Lebanon, Malaysia, Mexico, Morocco, the Netherlands, Nigeria, Oman, Pakistan, Palestinian Territories, Peru, the Philippines, Portugal, Qatar, Saudi Arabia, Singapore, South Africa, Spain, Sri Lanka, Switzerland, Taiwan, Thailand, Tunisia, the United Arab Emirates, the United Kingdom, the United States, Uzbekistan, and Vietnam.
• Required: Practical expertise in Python, including Pandas and scikit-learn.
• Required: Knowledge of applied statistics, experimental design, and machine learning.
• Required: Experience developing, tuning, and evaluating machine learning models in practical settings.
• Required: Strong data modeling skills across structured and unstructured data.
• Required: Clear written and verbal communication, including the ability to explain technical decisions.
• Helpful: Experience in technology, finance, or research organizations; open or cross-functional data science projects; or advanced academic or hands-on training in data science or applied mathematics.
• Prior AI experience is not required when you bring strong statistics, machine learning, experimentation, and quantitative reasoning skills.

You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.

AI training is the human work behind artificial intelligence, including reviewing model responses, writing prompts, and preparing examples that help models improve. People with strong technical expertise are especially useful when projects require careful analysis, reliable evaluation, and clear reference answers.