Data Analyst (AI Evaluation)

$30–60/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: US
Field: STEM and Coding

Review datasets and AI task outputs for accuracy, completeness, and compliance with detailed evaluation rubrics. This remote, US-based contract role emphasizes data quality judgment, consistent error classification, and clear documentation of ambiguous cases.

The work

You will review datasets and detailed task outputs, identify errors or instruction deviations, and apply structured evaluation criteria across high-volume assignments. Day to day, this means checking completeness and accuracy, recording defensible rationales, flagging recurring problems, and giving actionable feedback rather than silently correcting isolated issues.

What micro1 screens for

The screening is likely to focus on verifiable data review or quality assurance experience, precise use of written rubrics, and judgment when criteria do not fully resolve a case. Baseline spreadsheet fluency, including sorting, filtering, and simple formulas, is preferred. SQL or hands-on data querying is a plus, while prior data annotation, labeling, or rubric-based evaluation experience is advantageous. No prior AI experience is required.

Working approach

Strong candidates can distinguish a one-off error from a systematic quality issue, preserve consistency during repetitive review, and explain decisions in language another reviewer can act on. Expect follow-up questions about specific projects, the checks you performed, the evidence you used, and how you handled disagreements or incomplete guidance.

Logistics

This is a contractor role open to US-based candidates and performed remotely. The listing does not specify weekly hours, contract duration, or whether work is fully asynchronous, so confirm scheduling and workload expectations during the process.

Pay band: $30–60/hr

What they look for

Data Analysis
Data Review
Attention to detail
Handling ambiguity
Error spotting

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 you can provide a verifiable example with a defined dataset, review criteria, personal responsibilities, and measurable findings rather than describing analytics work in general.
A strong answer: Describe the data or deliverable, the criteria used, the volume reviewed, your exact role, and the types of defects you found. Include a concrete outcome such as a corrected process, reduced error rate, or documented escalation.
Where people lose it: Candidates lose points by discussing dashboards or reporting without showing that they personally validated records or judged outputs against stated criteria.

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

What stem and coding AI work pays

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