About this role
In this hourly, remote contractor role, you will work as an Economics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across economics-focused AI training projects. You will review AI-generated economics 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 economic accuracy, model reasoning, assumptions, data interpretation, calculation correctness, graph interpretation, policy nuance, 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 economics expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert 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 economics quality leadership will directly help improve the world’s premier AI models by ensuring that economics training data is accurate, analytically sound, well-explained, assumption-aware, 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.
Requirements
- Bachelor’s, Master’s, or PhD degree in Economics, Applied Economics, Econometrics, Public Policy, Finance, Statistics, Mathematics, or a closely related quantitative/social science field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
- 3+ years of experience in economics research, teaching, policy analysis, economic consulting, data analysis, econometrics, content review, or related analytical workflows.
- Strong understanding of microeconomics, macroeconomics, market structures, incentives, supply and demand, elasticity, externalities, monetary/fiscal policy, inflation, unemployment, trade, growth, and basic econometric reasoning.
- Ability to evaluate economics content against detailed rubrics and identify issues such as incorrect assumptions, flawed causal reasoning, wrong formulas, misleading graphs, unsupported policy claims, poor data interpretation, or oversimplified conclusions.
- Familiarity with tools or methods such as regression analysis, causal inference, economic modeling, Excel, R, Python, Stata, public datasets, policy briefs, and academic economics writing is preferred.
- Experience leading or supporting remote teams of analysts, researchers, reviewers, educators, annotators, 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, calibration tasks, and documentation.
- Experience with AI training, data annotation, LLM evaluation, economic QA, academic review, or rubric-based review is a strong plus.
Responsibilities
- Quality monitoring: Spot-check economics items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Economics review: Evaluate AI-generated economics explanations, calculations, graphs, policy analyses, data interpretations, and step-by-step reasoning for accuracy and clarity.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and economics-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around economic models, assumptions, formulas, graphs, causal claims, data interpretation, 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 economics 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 economics-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply economics-review guidelines consistently and understand updates as projects evolve.
- Risk review: Flag overconfident, misleading, politically biased, statistically weak, or assumption-free economic claims.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for economics AI training projects.
Skills
- Microeconomics
- Policy Analysis
- LLM Evaluation
- Data Interpretation
- Econometrics
- Economics
- AI Training
- Trainer Feedback
- Economic Analysis
- Macroeconomics
Additional skills
- Quality Assurance (QA)
- Economics Research
- Econometrics
- Microeconomics
- Macroeconomics
- Regression Analysis
- Causal Inference
- Economic Modeling
- Policy Analysis
- Public Policy
- Academic Writing
- Content Review
- Rubric-Based Evaluation
- Technical Writing
- Data Analysis
- Statistical Analysis
- R
- Python
- Stata
- Microsoft Excel
- Data Visualization
- Public Datasets
- Peer Review
- Editing & Proofreading
- Documentation
- Style Guides
- Project Management
- Remote Team Leadership
- LLM Evaluation
- Data Annotation
