> Markdown version of [/jobs/ext/3543812-data-scientist-talent-network-train-ai-models-part-time](https://www.wearedevelopers.com/jobs/ext/3543812-data-scientist-talent-network-train-ai-models-part-time). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Data Scientist Talent Network (Train AI Models Part Time

- **Company:** Mercor, Inc.
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Python (Programming Language), Machine Learning, SQL Databases, Feature Engineering, Machine Learning Operations
- **Published:** October 2, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2997660848-2

## About the Role

1+ years of professional data science experience

 - Experience at a leading technology, research, or quantitative firm (such as top FAANG, AI labs, top-tier quant funds, or equivalent)

 - Strong command of Python, SQL, statistical modeling, machine learning, experimentation and causal inference, and translating messy real-world data into rigorous analyses

 - Exceptional written communication skills, including the ability to convey technical findings clearly

 - A detail-oriented and consistent approach to evaluating complex work

 - Comfort receiving feedback and calibrating judgment against established standards

## Description

hackajob is collaborating with Mercor to connect them with exceptional professionals for this role.

Mercor is building a network of experienced data scientists for potential future projects with leading AI research organizations. These projects may focus on evaluating how effectively AI systems perform real-world data science work.

There is no immediate project opening, but qualified applicants may be contacted as relevant opportunities become available.

 2. Potential Responsibilities

Future projects may involve:

 - Designing precise, task-specific grading criteria for data science deliverables, including exploratory data analyses, statistical modeling work, machine learning pipelines, experimentation and A/B test write-ups, feature engineering, and technical reports or notebooks

 - Evaluating AI-generated or human-created work against established criteria

 - Providing detailed written justifications for evaluations and scores

 - Applying consistent, evidence-based judgment so that assessments are reproducible and defensible

 - Incorporating structured feedback from senior reviewers and iterating on submitted work


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