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# AI Trainer, Data Science

- **Company:** Brunel
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $83,200.0 - $260,000.0
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Data Analysis, Computer Vision, Information Engineering, Software Debugging, Python (Programming Language), Machine Learning, Raw Data, SQL Databases
- **Published:** October 27, 2026
- **Apply:** https://www.careerjet.com/jobad/usfad5d11e325cc8b18509312bfe6f1dbe

## About the Role

* Fluency in English (native or bilingual level)
 * 2+ years of professional applied data science experience, in industry or research
 * Working proficiency in Python and/or SQL, with the ability to write, debug, and explain your own analysis code
 * Experience owning a multi-step analysis end to end, from raw data through modeling and interpretation to a decision
 * The ability to judge whether a result is real, and to explain why an answer is wrong, not only that it is
 * Working comfort with AI tools, or the ability to pick them up quickly
 * A current, in progress, or completed Bachelor's in a quantitative field is preferred

## Description

Brunel is partnering with DataAnnotation to connect experienced Data Scientists with an innovative AI training program. This opportunity brings together hands-on data science expertise and artificial intelligence to help evaluate and improve how AI models handle real data science work. Selected experts will contribute their knowledge to the development of high-quality, technically accurate AI systems.
This opportunity is designed for practicing data scientists who take analyses from messy data to a defensible conclusion. It covers product and business analytics, applied machine learning, statistics and experimentation, forecasting, analytics engineering, and applied NLP or computer vision.
Engagement Details
Engagement
Fully remote, project-based work with a flexible schedule. The minimum is 10 hours per week with no weekly maximum, and full-time hours are available.
Pay rate, * Design challenging, realistic data science tasks from your own practice: an exploratory analysis of a messy dataset, an A/B test design and readout, or a model with proper validation

 * Run those tasks through frontier AI models and judge the output against your professional standard
 * Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short
 * Write detailed grading rubrics and tests that define what a correct deliverable must contain, and explain why a response passes or fails
 * Flag concrete failures with evidence: leakage, confounding, wrong joins, misapplied tests, code that runs but doesn't do what was asked, fabricated or ignored data, and off-brief interpretation
 * Contribute across analytics, machine learning, experimentation, and data engineering, and review and refine tasks built by other experts