🌍Competitive Data Scientist (Kaggle), Remote- Contract
Summary
Fully remote contract (~15 hrs/week) for a data scientist with competitive ML experience (Kaggle, DrivenData) to build reproducible task environments, reference solutions, and evaluation tests, then run coding agents against them and refine tasks based on failures. Core skills: Python, ML engineering, data science, and mathematics.
Role Title: Competitive Data Scientist (Kaggle)
Location: Global, fully remote
Job Type: Contract (~15 hours per week)
Schedule: Flexible-you choose the hours and days you work, including weekends if desired
Required Skills:
ML Engineering
Data Science
Mathematics
Coding
We're looking for data scientists, machine learning practitioners, and applied mathematics experts with competitive 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.
Huge Pluses:
Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code

