Job description
**Pay:** $60-$120 per hour (USD).
**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**
1. Build reproducible environments, prepare relevant data and dependencies, and implement reference solutions.
2. Develop tests and evaluation criteria that reliably distinguish correct solutions from incomplete or flawed approaches.
3. Run coding agents against tasks, investigate their failures, and refine the tasks based on observed behavior.
4. Identify implementation errors, flawed assumptions, data leakage, and other issues that affect correctness.
5. Document task requirements, solution reasoning, and verification steps clearly.
**What We’re Looking For**
1. Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions. (Kaggle, DrivenData, etc.)
2. Strong problem-solving ability and a clear understanding of the methods you use.
3. 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**
1. Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code
Ready to apply?
You'll be taken to Micro1 career page to submit your application. We'll also add this to your tracker if you want.

Originally posted via 4 Day Week. View source ↗
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