Competitive Data Scientist (Kaggle)

micro1; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

micro1 is seeking competitive data scientists, ML practitioners and applied mathematics experts with Kaggle-style competition experience. You will design tasks with reproducible environments, reference solutions, and robust verification. Global, remote work with flexible hours.

We value strong programming ability (Python), track records in data science competitions, and clear mathematical reasoning. You’ll build tests, evaluate solutions, and document results to ensure reliable scoring across

Qualifications

• 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.

Responsibilities

• 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.

Skills

Data Science

Machine Learning

Quantitative Modeling

Programming

Tools

Python

Testing

Debugging

Job description

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