Data & Charts Expert

$25–50/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Worldwide
Field: Other

Create chart-based questions that test whether AI systems can interpret quantitative information accurately. You will select real charts from your field, establish correct answers and explain the reasoning in clear written English.

The work

This remote contract role involves selecting graphs, plots or dashboards from your professional, academic or research field. You will write questions that require calculations, comparisons, trend interpretation or synthesis across multiple data series, then provide the correct answer with a step-by-step explanation. You should also identify charts or questions that are ambiguous rather than forcing a single answer.

What micro1 screens for

The AI interview is designed to verify at least two years of experience working with charts and quantitative data, plus a degree in a quantitative or technical field or equivalent hands-on experience. Expect follow-up questions about axes, scales, units, legends, annotations, error bars, time periods and statistical features. You must be able to explain calculations clearly in written English. Experience creating exams, assessments or analytical exercises is preferred, not required.

Practical details

The opportunity is fully remote and open globally. Work is asynchronous and the weekly commitment is flexible, with no typical number of hours stated. The selection process includes screening questions, an AI interview, profile review and onboarding by Talent Ops.

Pay band: $25–50/hr

What they look for

Chart interpretation accuracy
Multi-step quantitative reasoning
Question design / unambiguous task construction
Domain-specific terminology precision
remote collaboration

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 verifies the duration, setting and substance of your chart work rather than relying on a general claim of data literacy.
A strong answer: Give a dated account of professional, academic or research work, naming the chart types, data sources and decisions or analyses for which you used them. Distinguish between creating charts, interpreting them and checking other people's interpretations.
Where people lose it: Candidates lose credibility when they mention software or dashboards but cannot identify a chart they interpreted, the variables shown or the conclusion drawn.

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