Data Visualization Scientist is a remote review track for evaluating AI outputs across research science reasoning, calculations, and research workflows.
Data Visualization Scientist is a remote review track for evaluating AI outputs across research science reasoning, calculations, and research workflows. Reviewers grade derivations and assumptions, reproduce key results, and document the correct method so the modeling team can train on it.
Research science models live or die on whether their derivations actually hold up under scrutiny. AuraOne uses scientific specialists to grade outputs the way a peer reviewer would — checking assumptions, reproducing key steps, and capturing the right method alongside the wrong one.
Bring scientific and technical domain expertise into AI reasoning, research, and dataset review.
Responsibilities
Review AI outputs against current research science methods, conventions, and prior work for Data Visualization Scientist assignments.
Reproduce or sanity-check key derivations, calculations, or experimental claims.
Flag dimensional, methodological, and citation errors with structured severity tags.
Capture the corrected reasoning or worked example so the modeling team can train on it.
Adjudicate disputed answers against textbooks, papers, or community standards.
Maintain reviewer-quality scores in inter-rater calibration cycles.
What you should bring
Graduate-level training or equivalent applied experience in research science or a closely related field for Data Visualization Scientist work.
Hands-on experience publishing, teaching, or advising on the topic at a professional level.
Comfort applying multi-page rubrics consistently across long batches.
Clear written reasoning that cites methods, papers, or worked examples.
Reliable async availability for at least 10 hours per week.
Role signals
Example tasks
Reproduce a research science derivation from a model output and flag any algebraic or dimensional errors.
Grade a model's literature summary against the cited papers and rate the citation quality.
Adjudicate a disputed answer between two reviewers using textbook methods.
Audit a 25-row batch for rubric consistency and report drift to the program lead.
Useful experience
PhD, postdoc, or industry research experience in the topic area.
Prior work reviewing AI-assisted research tooling and its failure modes.
Multilingual fluency for non-English papers and corpora.
Compensation and schedule
$125 / hr
Expected arrangement: contractor, with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.
Skills used in matching
Scientific reasoning
Method validation
Citation review
Quantitative analysis
Research science
AI model evaluation
Application process
Submitting this form sends your application directly to the recruiting team for this specialist cohort.
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