Lead AI Trainer & Data Scientist (Remote)

DataAnnotation; San Francisco (CA); On-site; USD 50,000 - 83,000; Full time

Benefits offered by this job

Fully remote

Flexible schedule

Competitive hourly pay

Job summary

DataAnnotation is seeking a Principal Data Scientist - AI Trainer to evaluate AI outputs and develop training materials for next-generation models. This fully remote role enables collaboration across time zones with a flexible schedule.

You will assess quantitative work for correctness, design training problems for training and benchmarking, and provide clear documentation and well-documented analytical code to guide improvements.

Qualifications

• 2+ years in a quantitative role or research environment (data science, statistics, economics, finance, physics, biology, epidemiology, OR)
• Some coding experience with end-to-end analytical code
• Practical experience applying statistical methods, predictive modeling, and experiment design (A/B testing, hypothesis testing, regression, classification, time-series forecasting)
• Fluency in English with strong writing ability
• Bachelor’s degree in a quantitative field; Master’s/PhD is a plus
• Credentials such as Kaggle ranking or AWS/GCP ML certifications are a plus

Responsibilities

• Evaluate AI-generated quantitative outputs for correctness and real-world validity
• Design and solve quantitative problems used to train and benchmark AI systems
• Produce clear technical explanations and well-documented analytical code
• Deliver feedback to influence the next generation of AI models for quantitative reasoning

Skills

Quantitative experience

Coding experience

English fluency

Education

Bachelor's degree in a quantitative field

Master's degree or PhD is a plus

Tools

PayPal

AWS

GCP

Kaggle

Job description

DataAnnotation is seeking a Principal Data Scientist - AI Trainer to evaluate AI outputs and develop training materials for next-generation models. This fully remote role enables collaboration across time zones with a flexible schedule.

You will assess quantitative work for correctness, design training problems for training and benchmarking, and provide clear documentation and well-documented analytical code to guide improvements.