Competitive Data Scientist
micro1 · 100% remote · Contract · Posted
- Pay
- $60–120/hr
- Location
- Worldwide
- Languages
- English
- Hours
- 15 hours per week
- Openings
- 50
- Level
- Expert
Summary
Create data science tasks with reproducible environments and reference solutions. Test tasks by running coding agents, refine based on failures, and document everything.
What you'll do
- Build reproducible environments, prepare data and dependencies, and implement reference solutions
- Develop tests and evaluation criteria that distinguish correct from incomplete solutions
- Run coding agents against tasks, investigate failures, and refine tasks
- Identify implementation errors, flawed assumptions, and data leakage
- Document task requirements, solution reasoning, and verification steps
Requirements
- Demonstrate experience in data science, ML, quantitative modeling, or research competitions like Kaggle, DrivenData
- Apply strong problem-solving ability and clear understanding of methods
- Write practical code to implement and verify your own solutions
- Optionally, have strong coding experience in Python including testing and debugging
Skills
- ML Engineering
- Data Science
- Mathematics
- Coding
Full description
Role Title: Competitive Data Scientist
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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