Revenue, Demand & Supply Planning Professional Evaluator

You will evaluate AI-generated responses and create training data based on real retail revenue, demand, and supply planning work. Your feedback will help assess whether AI content is accurate, relevant, and practical.

The work is ongoing, project-based, remote, and asynchronous. Review the duties and qualifications before applying to confirm the role fits your background.

• Evaluate AI responses against revenue, demand, inventory, pricing, and supply planning workflows.
• Review demand forecasting and in-season reforecasting content for accuracy and relevance.
• Use practical knowledge of inventory planning, supply planning, pricing, promotions, and margin optimization.
• Assess freight, logistics, cost-management, and invoice-auditing topics when relevant.
• Provide clear, detailed written feedback and make well-supported judgments independently.

What it pays and takes

It does require substantial professional planning experience and strong written communication.

• Pay: Up to $80 per hour.
• Work type: Part-time contractor work on an ongoing, project-based basis.
• Hours: 20+ hours per week when an active project is available.
• Location: Open to candidates in the United States.
• Language: Fluent English.
• Experience: At least five years in revenue, demand, or supply planning in retail or consumer-facing industries, or equivalent end-to-end planning experience.
• Relevant backgrounds include Demand Planner, Supply Chain Manager, Pricing Analyst, or similar planning and analytics roles.
• Required knowledge includes demand forecasting, in-season reforecasting, inventory planning, and supply planning processes.
• Helpful experience includes pricing, promotions, margin optimization, freight, logistics, cost management, or invoice auditing.
• You should have close attention to detail and be able to work independently.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human side of building AI. Contributors review and write examples, rate model responses, and provide feedback so AI systems can produce more accurate and useful results.