Remote AI Trainer – Quantitative Reasoning & Model Feedback

DataAnnotation; Town of Texas (WI); On-site; USD 69,000 - 138,000; Part time

Benefits offered by this job

Fully remote

Flexible schedule

PayPal payments

Job summary

DataAnnotation is seeking a fully remote AI Trainer to shape how advanced systems handle quantitative, data-driven reasoning. You will review AI-generated work, validate technical soundness against real-world expectations, and provide feedback to influence future model performance.

Projects span statistics, predictive modeling, and experimental or decision-making problems. You will deliver clear explanations and well-documented analytical code, shaping the next generation of AI models built for

Qualifications

• 3+ years of hands-on experience in a quantitative role or research environment.
• Some coding experience with end-to-end analytical code.
• Experience with statistical methods, predictive modeling, and experiment design (A/B testing, hypothesis testing, regression, classification, time-series forecasting).
• Fluency in English and strong writing skills.
• Bachelor’s degree in a quantitative field; advanced degree is a plus.
• Credentials such as Kaggle ranking or ML certifications are a plus.

Responsibilities

• Assess AI-generated quantitative work for technical accuracy and real-world validity.
• Develop and solve quantitative problems to train and benchmark AI systems across forecasting, experimental analysis, optimization, and statistical inference.
• Produce clear technical explanations and well-documented analytical code.
• Deliver feedback to influence the next generation of AI models for quantitative reasoning.

Skills

Quantitative analysis

Statistics

Programming experience

English writing

Data science

Education

Bachelor's degree in quantitative field

Advanced degree (Masters/PhD)

Kaggle ranking

ML certifications

Job description

DataAnnotation is seeking a fully remote AI Trainer to shape how advanced systems handle quantitative, data-driven reasoning. You will review AI-generated work, validate technical soundness against real-world expectations, and provide feedback to influence future model performance.

Projects span statistics, predictive modeling, and experimental or decision-making problems. You will deliver clear explanations and well-documented analytical code, shaping the next generation of AI models built for