AI Evaluation
Financial Quality Assurance Lead (QAL)
Status: Open
- Pay
- $90/hr
- Location
- Remote — India, The United States
- Employment type
- Contractor
About this opportunity
In this hourly, remote contractor role, you will work as a Finance Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across finance AI training projects. You will review AI-generated finance content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for financial accuracy, analytical reasoning, calculation correctness, terminology quality, market and accounting awareness, risk sensitivity, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong finance expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote finance-review teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your finance quality leadership will directly help improve the world’s premier AI models by ensuring that finance training data is accurate, logically sound, clearly explained, appropriately cautious, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
Required skills
- Documentation
- LLM evaluation
- Risk Review
- Trainer Feedback
- AI Training
- Financial QA
- investment analysis
- Accounting
- Finance
- Financial Analysis
What you'll do
- Quality monitoring: Spot-check finance items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Financial review: Evaluate AI-generated finance explanations, financial calculations, investment analyses, accounting interpretations, valuation workflows, risk discussions, market summaries, and problem-solving steps for accuracy, clarity, and appropriate caution.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and finance-review-specific standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around financial reasoning, formulas, assumptions, accounting treatment, valuation logic, risk caveats, terminology, and rubric interpretation.
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
- Documentation: Create and maintain finance project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and finance-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply finance-review guidelines consistently and understand updates as projects evolve.
- Risk and compliance review: Flag unsafe, misleading, overconfident, or non-compliant finance outputs, especially where the content could be interpreted as personalized investment, tax, accounting, lending, insurance, or financial advice.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for finance AI training projects.
Requirements
- Bachelor’s or Master’s degree in Finance, Accounting, Economics, Business Administration, Financial Engineering, Quantitative Finance, or a closely related field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear finance-review feedback in English.
- 3+ years of professional experience in finance, financial analysis, investment analysis, corporate finance, accounting, banking, valuation, FP&A, risk management, audit, fintech, financial operations, or related workflows.
- Strong understanding of core finance topics such as financial statements, valuation, corporate finance, capital markets, investments, portfolio concepts, risk analysis, budgeting, forecasting, accounting principles, financial ratios, and time value of money.
- Ability to evaluate finance content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, unsupported investment claims, missing caveats, hallucinated financial facts, misleading risk statements, incomplete reasoning, or unclear explanations.
- Familiarity with finance workflows or tools such as Excel/Google Sheets modeling, financial statements, valuation models, DCF, comparables, budgeting/forecasting, KPI dashboards, market research, financial databases, accounting systems, or financial reporting is preferred.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, analysts, accountants, finance writers, researchers, educators, or QAs is strongly preferred.
- Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
- Experience with AI training, data annotation, large language models, prompt/response evaluation, financial content QA, or rubric-based LLM evaluation is a strong plus.
- Professional certifications such as CFA, CPA, ACCA, FRM, CMA, CA, or equivalent are a plus.
Compensation
Rates by country (hourly): India: up to USD 20; The United States: up to USD 90