Competitive Data Scientist

You will create and validate competitive data science tasks used to assess coding agents and data science ability. The work combines quantitative modeling, Python programming, debugging, testing, and careful review of whether solutions are genuinely correct.

You will turn complex data science reasoning into reproducible tasks with clear requirements, reference solutions, and reliable checks.

• Build reproducible environments with the required data, dependencies, and execution steps.
• Create verified reference solutions that show how each task should be completed.
• Write tests and evaluation criteria that separate correct solutions from incomplete or flawed approaches.
• Run coding agents against tasks, investigate failures, and improve task design based on what you find.
• Identify implementation errors, flawed assumptions, data leakage, and other problems that affect correctness.
• Document task requirements, solution reasoning, and verification steps clearly.

What It Pays and Takes

This is a part-time contractor role for people who can independently implement, test, and verify data science solutions. The listing is marked entry level, with practical programming and competition experience required.

• $60 to $120 per hour equivalent, paid per task that meets the project specifications.
• Approximately 15 hours per week, with a flexible schedule.
• Open to candidates in the listed countries across the Middle East, Asia, Europe, Africa, North America, and South America.
• Fluent English is required.
• Experience in data science, machine learning, quantitative modeling, or research competitions.
• Competition experience with platforms such as Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, or CodaLab/Codabench.
• Strong problem-solving skills and a clear understanding of the methods used in your solutions.
• Practical programming skills for implementing, testing, and verifying solutions independently.
• Strong Python skills, including testing, debugging, and working with unfamiliar code.

Helpful Background

Published competition solutions, a strong competitive data science profile, or a notable competition rank can help show the depth of your practical expertise. Backgrounds in applied mathematics, machine learning engineering, or quantitative research are also relevant when paired with hands-on implementation and verification skills.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training work is the human side of building artificial intelligence. People create examples, review model outputs, and test whether systems follow instructions, so projects that require strong technical judgment pay for specialized experience.