Materials Characterisation Scientist

$50/hr

rate as listed

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

Create research-level materials characterisation problems using evidence from real instrument data and analysis workflows. This remote contract role requires hands-on data analysis experience with Python and clear technical documentation.

The work

You will write research-level problems that test AI models on materials characterisation data. Assignments may involve XRD, indentation, impedance, corrosion, rheology or battery cycling, with an emphasis on realistic interpretation, defensible calculations and clearly documented reasoning.

What the platform screens for

AfterQuery looks for hands-on experience operating instruments or analysing your own experimental data. Working knowledge of Python is required, while experience with methods such as Rietveld refinement, Oliver-Pharr analysis, EIS fitting, ASTM test standards and reusable analysis scripts is preferred. A Master's or PhD in materials science, metallurgy or a related field, with lab characterisation work, is preferred, as is public code, a thesis or papers demonstrating technical work. Prior data annotation, data labeling or AI/ML evaluation work is also preferred.

Working format

The work is fully remote, independent and task-based, with no fixed weekly schedule. It can be completed asynchronously around other commitments, but each accepted task is described as requiring about six hours. Contractors manage their own workload and must be able to work without visa sponsorship; students on F-1 visas, including OPT or CPT, are not eligible.

Verification and compensation

Accepted experts undergo standard identity and background verification. Pay band: $50/hr

What they look for

Hands-on analysis of materials characterisation data
Working knowledge of Python for instrument data analysis
Research-level problem design in materials science or metallurgy
Clear technical writing and documentation
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 you can trace a real experimental workflow from acquisition through preprocessing and interpretation rather than discussing characterisation only at a theoretical level.
A strong answer: Use one specific project and identify the material, instrument settings, raw files, cleaning or calibration steps, analysis libraries or scripts, uncertainty checks and the conclusion supported by the data. Distinguish what you personally operated, coded and interpreted.
Where people lose it: Candidates lose points by naming techniques and Python packages without explaining how raw measurements became a defensible result.

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