AI training contract
Competitive Data Scientist (Kaggle)
AI & Machine Learning · USD 60-120 an hour. · 50 openings
Competitive Data Scientist (Kaggle) is contract work for micro1, reviewing and correcting what AI models produce in ai & machine learning. The output of the job is expert judgement written down: a model gives an answer, someone who knows the subject decides whether it is right and explains why.
What it requires. No credential is stated on this listing beyond experience in the field.
What it pays. USD 60 to USD 120 an hour, as published by micro1. Rates in this category track how rare the credential is rather than how hard the work is.
Where. The listing says this role is global and fully remote. micro1 says it hires talent from around the world, except people who reside in Afghanistan, Belarus, China, Cuba, Democratic Republic of the Congo, Hong Kong, Iran, Iraq, Libya, Macao, Myanmar, North Korea, Russian Federation, Somalia, South Sudan, Sudan, Syria, Venezuela, Ukraine, Yemen, and Ethiopia. That rule comes from micro1's own FAQ at www.micro1.ai/experts, read on 2026-10-05. Requirements can still differ by role, so check the application page on micro1 before you apply.
micro1 lists 50 openings on this role.
Skills named: ML Engineering, Data Science, Mathematics, Coding.
Applications are made on micro1, not here. WFA Digital is independent of micro1 and may receive a referral fee if you are hired, at no cost to you.
Full listing, as published by micro1
Role Title: Competitive Data Scientist (Kaggle) Pay: $60–$120/hour Location: Global, fully remote Job Type: Contractor (~15 hours per week) Schedule: Flexible-you choose the hours and days you work, including weekends if desired We're looking for data scientists, machine learning practitioners, and applied mathematics experts with competitve Data Science experience. (Kaggle, DrivenData, etc.) You’ll draw on your competition experience, mathematical reasoning, and programming skills to create tasks that require more than a plausible answer. Each task should have a reproducible environment, a verified reference solution, and reliable checks for successful completion. What You’ll Do Build reproducible environments, prepare relevant data and dependencies, and implement reference solutions. Develop tests and evaluation criteria that reliably distinguish correct solutions from incomplete or flawed approaches. Run coding agents against tasks, investigate their failures, and refine the tasks based on observed behavior. Identify implementation errors, flawed assumptions, data leakage, and other issues that affect correctness. Document task requirements, solution reasoning, and verification steps clearly. What We’re Looking For Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions. (Kaggle, DrivenData, etc.) Strong problem-solving ability and a clear understanding of the methods you use. Practical programming ability sufficient to implement and verify your own solutions. Relevant competition platforms include Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, and CodaLab/Codabench. Huge Pluses Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code
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Rate, openings and requirements checked 2026-10-19 against micro1's own public postings.