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# TELECOMMUTE Data Science - AI Trainer

- **Company:** DICE INC
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $156,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Information Engineering, Python (Programming Language), Machine Learning, SQL Databases
- **Published:** August 1, 2026
- **Apply:** https://www.dice.com/job-detail/d5593fd2-692f-47c1-aa8d-e20e9ae9594d

## About the Role

* Minimum 2 years but ideally 5+ years of professional data science experience (analytics, machine learning, statistical modeling, data engineering, or similar applied work) in industry or research. Undergraduate study does not count toward the experience minimum.
 * Working proficiency in Python and/or SQL; comfort reading and writing analysis code is expected
 * Strong written communication skills; ability to explain complex concepts clearly and concisely
 * Comfortable working independently in a remote, self-directed environment
 * Available to commit 30 40 hours per week for the full 12-week engagement
 * Based in the United States
 * Advanced degree in a quantitative field is a plus - practical expertise matters more than credentials

## Description

Dice.com is seeking experienced data science professionals to join a 12-week AI training sprint as contract contributors. In this role, you will apply your domain expertise to evaluate, annotate, and improve AI-generated responses related to data analysis, statistical reasoning, machine learning concepts, and applied modeling and to build challenging, realistic data science tasks used to train AI systems. Your work directly shapes the accuracy and reliability of AI tools used by data professionals every day., * Review and evaluate AI-generated responses to data science prompts analysis, modeling, statistics, and code for correctness and rigor

 * Design realistic, challenging data science problems used to train AI systems
 * Write clear, expert-level solutions and explanations that serve as model training examples
 * Flag errors, flawed reasoning, or ambiguous outputs and provide corrective guidance
 * Maintain consistent quality and throughput throughout the sprint engagement


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