Remote Data Scientist: AI Trainer for Model Benchmarking

DataAnnotation; Farmers Branch (TX); On-site; USD 69,000 - 138,000; Full time

Benefits offered by this job

Fully remote

Job summary

DataAnnotation seeks a Data Scientist - AI Trainer to assess AI-generated quantitative work and craft training tasks that benchmark AI reasoning. This fully remote role offers flexible scheduling and a platform to influence how AI models approach data across forecasting, experimental analysis, optimization, and statistical inference.

Responsibilities include evaluating AI-produced work, designing quantitative problems for training and benchmarking, writing clear explanations, and providing

Qualifications

• 2+ years in a quantitative role or research environment.
• Some coding experience with end-to-end analytical code.
• Experience with statistical methods, predictive modeling, and experiments (A/B testing, hypothesis testing, regression, classification, time-series forecasting).
• Fluency in English with strong writing skills.
• Bachelor's degree in a quantitative field; Master’s or PhD is a plus.
• Credentials like Kaggle rankings or cloud ML certifications are beneficial.

Responsibilities

• Evaluate AI-generated quantitative work for technical accuracy and real-world validity.
• Design and solve quantitative problems to train and benchmark AI systems.
• Write clear technical explanations and maintain well-documented analytical code.
• Provide feedback to inform the next generation of AI models built for quantitative reasoning.

Skills

Quantitative background

Statistical methods

A/B testing

English writing

Coding experience

Education

Bachelor's degree in quantitative field

Master's or PhD in a quantitative field

Tools

AWS ML certifications

GCP ML certifications

Job description

DataAnnotation seeks a Data Scientist - AI Trainer to assess AI-generated quantitative work and craft training tasks that benchmark AI reasoning. This fully remote role offers flexible scheduling and a platform to influence how AI models approach data across forecasting, experimental analysis, optimization, and statistical inference.

Responsibilities include evaluating AI-produced work, designing quantitative problems for training and benchmarking, writing clear explanations, and providing