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Competitive Data Scientist (Kaggle)

STEP 1 · PRACTICE FIRSTTake a free AI practice interview for this kind of roleGet my practice interview ↓
STEP 2 · APPLYApply on micro1 when you’re readyApply on micro1 →

You apply directly on micro1's site. Applying is free. Posted Oct 12, 2026.

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STEP 1 · PRACTICE BEFORE YOUR REAL INTERVIEW

Get your free practice interview for this role

micro1 screens applicants online, often with an AI interview. micro1 offers a free, 18-minute AI practice interview with feedback, and we picked the one closest to this role: Data Scientist. It covers:

Statistical analysisMachine learningData visualization

Here’s your practice interview

Allow your microphone when it asks, find a quiet spot, and give yourself about 20 minutes. We’ve also emailed you the link and the free ebook.

Step 2: when you feel ready, apply.

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The practice interview is free and run by micro1. Land Remote Jobs is an independent job board, not part of micro1, Mercor or Turing. Practising helps you prepare; it can’t guarantee the result of your real interview. Want more tips? Read our free micro1 interview guide.

STEP 2 · APPLY

Apply for this role

You apply on micro1's own site. It’s free, and you can apply even if you skip the practice.

Apply on micro1 →

Who this role suits

Working engineers who want flexible, project-based remote work that uses their coding and review skills. A strong portfolio or GitHub helps.

ML EngineeringData ScienceMathematicsCoding

Full 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

Last checked open on Oct 28, 2026. Job description provided by micro1.

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