Competitive Data Scientist

AuraOne; United States; Remote; USD 141,000 - 190,000; Full time

Job summary

AuraOne is seeking a Competitive Data Scientist to remotely review AI outputs across research science reasoning, calculations, and workflows. Reviewers grade derivations, reproduce key results, and document the correct method so the modeling team can train on it.

You will work as an independent contractor with at least 10 hours per week, evaluating outputs against textbooks, papers, or standards, flagging errors, and capturing corrected reasoning to improve model training.

Qualifications

• Graduate-level training or equivalent in research science.
• Experience publishing, teaching, or advising on the topic at a professional level.
• Ability to apply rubrics consistently across long batches.

Responsibilities

• Review AI outputs against current research science methods and prior work.
• Reproduce or sanity-check key derivations, calculations, or claims.
• Flag dimensional, methodological, and citation errors with structured severity tags.
• Capture corrected reasoning or worked examples for model training.
• Adjudicate disputed answers against textbooks, papers, or community standards.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Scientific reasoning

Method validation

Citation review

Quantitative analysis

Research science

Education

Graduate-level training or equivalent

Job description

Category: Scientific AI & Domain Experts · Pay: $120 / hr · Location: Remote — US-eligible · Contractor

Competitive Data 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.

Why this role matters

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.

Responsibilities

• Review AI outputs against current research science methods, conventions, and prior work for Competitive Data 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.

Qualifications

• Graduate-level training or equivalent applied experience in research science or a closely related field for Competitive Data 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.

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.

Nice to have

• 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.

Skills

• Scientific reasoning
• Method validation
• Citation review
• Quantitative analysis
• Research science

Work model

Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

Compensation

Hourly rate confirmed after the interview process.