Competitive Data Scientist

$60–120/hr

rate as listed

Workload: ~15 hrs/week
Engagement: Project-based
Location: Worldwide
Field: STEM and Coding

Create reproducible data science tasks, verified reference solutions, and tests that expose incomplete or incorrect agent outputs. This remote contract role suits practitioners with experience in competitions such as Kaggle, DrivenData, Zindi, or AIcrowd.

The work

You will turn data science and quantitative modeling problems into coding tasks with reproducible environments, prepared data, explicit dependencies, and verified reference solutions. Day to day, this includes defining evaluation criteria, running coding agents, investigating failures, and refining tasks so that plausible but flawed solutions do not pass.

What micro1 screens for

The screen focuses on demonstrated competition experience, mathematical and methodological depth, and the ability to implement and verify your own solutions. Expect follow-up questions about model validation, metric selection, data leakage, reproducibility, debugging, and how you would distinguish a correct solution from one that exploits assumptions in the task. Python is a strong plus, particularly for testing, debugging, and working with unfamiliar code.

Logistics

This is a global, fully remote contractor role at approximately 15 hours per week. Hours and working days are flexible, including weekends if desired. The listing describes self-scheduled work and does not specify required synchronous hours.

Pay band: $60–120/hr

What they look for

ML Engineering
Data Science
Mathematics
Coding

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether your competition experience is concrete and whether you can connect validation, feature, model, or optimization decisions to measured outcomes.
A strong answer: Anchor the answer in a named competition or clearly described equivalent, including the dataset, metric, validation design, approach, and result. Identify at least one decision that changed performance and explain why rather than merely listing models tried.
Where people lose it: Candidates lose points by describing a generic notebook workflow without a competition, metric, validation scheme, or attributable result.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

STEM AND CODING on Sidequest

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