Ocean, Atmosphere & Earth Observation Scientist

$50/hr

rate as listed

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

Create research-level AI evaluation problems using experience with raw oceanographic, atmospheric or geoscience data. The contract is remote and flexible, with task-based work completed independently.

The work

You will write accurate, well-documented research problems that test AI models in oceanography, atmospheric science, geoscience or a related discipline. Problems should draw on practical experience processing raw observations from research cruises, instrument deployments, surveys or field campaigns, including calibration, quality control and instrument drift.

What AfterQuery screens for

The platform looks for evidence that you have worked directly with field or instrument data rather than only climate-model or reanalysis products. Python is listed as a technical requirement for processing and quality-checking data. A PhD or Master's degree plus field campaign experience is preferred, as are public code, a thesis or papers demonstrating technical work and prior data annotation, data labeling or AI/ML evaluation experience.

Contract logistics

This is fully remote, independent-contractor work with no fixed weekly schedule. Tasks are described as taking about six hours, and experts choose how many to accept while managing their own time. Candidates must be able to contract without visa sponsorship, and students on F-1 visas, including OPT or CPT, are not eligible. Accepted experts undergo standard identity and background verification.

Pay band: $50/hr

What they look for

Hands-on processing of raw field, survey or instrument data
Working knowledge of Python for data processing and quality control
Oceanography, atmospheric science, geoscience or related subject expertise
Ability to design clear, research-level technical problems
Calibration, data-quality and instrument-drift judgment
Independent task and time management

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 field-data experience is direct and verifiable, including your role, the instrument or platform, the measured variables and the resulting data product.
A strong answer: Anchor the account to a named or clearly bounded project and distinguish your own responsibilities from those of the wider team. Include the instrument, raw format, processing stages and a concrete output such as a calibrated time series, profile collection or gridded survey product.
Where people lose it: Candidates lose points by describing the campaign's scientific goals without showing which raw files they handled or what processing they personally performed.

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