Science & Research Evaluators
Partner with researchers, PhD holders, and lab scientists to evaluate AI models on scientific reasoning, methodology, and research integrity.
Scientific AI evaluation demands researchers who can assess not just factual accuracy, but methodological soundness, statistical validity, and whether AI-generated content meets the standards that the scientific community expects.
Key Evaluation Areas
Experimental Design
Study methodology, control groups, variable isolation, sample sizing, and statistical power analysis.
Data Analysis & Statistics
Statistical test selection, results interpretation, effect size evaluation, and reproducibility assessment.
Literature Review
Source credibility, citation accuracy, synthesis of conflicting findings, and identification of research gaps.
Scientific Writing
Clarity of methods sections, accuracy of results reporting, appropriate conclusions, and adherence to field conventions.
Why Science Evals Are Critical
- AI confidently generates plausible-sounding claims that contradict established findings: domain experts catch this
- Statistical reasoning errors in AI outputs can propagate through downstream research and decision-making
- Citation hallucination is a well-documented problem that only subject matter experts can reliably detect
- Field-specific conventions and standards vary widely: what's rigorous in biology may not apply in physics
Evaluator Requirements
- PhD or equivalent research credentials in a scientific discipline
- Peer-reviewed publication track record
- Active involvement in research or academic work
- Strong methodological training and statistical literacy
- Commitment to scientific integrity and responsible AI evaluation
Hold AI to Scientific Standards
Scientific AI needs the same rigor as peer review. Join our network of research evaluators.
Apply as an Evaluator