Finance AI Auditor - Quant Research & Systematic Trading

Auraone; United States; Remote; USD 234,000 - 303,000; Full time

Job summary

AuraOne is seeking a Finance Expert — Quant Research & Systematic Trading to remotely review AI outputs for finance and risk workflows, ensuring citations, severity tagging, and corrected calculations accompany each evaluation.

You will re-perform calculations, flag rounding or classification errors, and document the corrected workpapers so the modeling team can train on them. Strong credentials and async availability are required.

Qualifications

• Direct working experience with finance and risk on real engagements, transactions, or audits for Finance Expert — Quant Research & Systematic Trading work.
• A CPA, CFA, or ACA. A comparable credential or equivalent applied experience also works.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that cites standards, regulations, or firm policy.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Review AI outputs against current finance and risk standards, regulations, and firm policy for Finance Expert — Quant Research & Systematic Trading assignments.
• Re-perform calculations and flag rounding, classification, or treatment errors.
• Tag compliance, reconciliation, and disclosure issues with structured severity scores.
• Capture the corrected workpaper or narrative so the modeling team can train on it.
• Adjudicate disputed treatments against published standards or firm guidance.
• Maintain reviewer-quality scores in inter-rater calibration cycles.

Skills

Financial analysis

Audit-grade review

Regulatory standards

Risk assessment

Finance and risk

Education

CPA / CFA / ACA

Job description

AuraOne is seeking a Finance Expert — Quant Research & Systematic Trading to remotely review AI outputs for finance and risk workflows, ensuring citations, severity tagging, and corrected calculations accompany each evaluation.

You will re-perform calculations, flag rounding or classification errors, and document the corrected workpapers so the modeling team can train on them. Strong credentials and async availability are required.