$90–120/hr · Mercor · Part time, 40 hours a week
Operations expert who creates training data and evaluates AI recommendations by translating years of management experience into specific benchmarks and feasibility assessments.
What you would do
- Write operational problems grounded in real work you have managed, with constraints, process details, and reference solutions
- Evaluate whether AI-generated recommendations are executable given organizational structure, budget, and approval requirements
- Grade model-written operational analysis for analytical rigor and practical applicability
- Identify where model recommendations break down due to vendor constraints, existing contracts, or headcount realities
- Provide concrete feedback that helps AI systems learn when perfect analysis encounters operational friction
Who they want
- Professional experience running or analyzing a major business function such as sales, operations, supply chain, or strategy
- Track record of successfully executing operational change or process improvement
- Clear written communication skills, since most work involves documenting your reasoning
- Comfort with ambiguous projects and willingness to flag when instructions need clarification
- Part-time availability; specific hours flexible and adjusted per project scope
Main skills
What the interview asks about
1.Spotting impractical but logical recommendations
AI often proposes improvements that are analytically correct but fail because of organizational realities; distinguishing these cases is central to this role's value.
For example: “An AI recommends consolidating four regional sales offices to two, eliminating 15 managers and reassigning 300 reps to corporate. What implementation risks would you flag? What feasibility grade would you give?”
2.Validating metrics against outcomes
AI systems can optimize for the wrong metrics; interviewers need to know you'll catch recommendations that improve a measured indicator without improving what actually matters.
For example: “An AI recommends cutting support response time from 24 to 4 hours to boost retention by 12%. Does response time actually drive retention in your experience, or does the model miss what customers care about?”
3.Writing clear operational case studies
Your lived experience is only valuable if you can translate it into teaching examples for AI systems; interviewers assess your ability to write specifics and not generalities.
For example: “You consolidated three supply chain distribution centers into two. Write the operating problem you'd create for AI training, including the constraint, the current process, and a number-based reference answer.”
4.Recognizing approval and budget constraints
Many strategic recommendations fail not because they don't work analytically but because they require budget approvals, contract renegotiations, or stakeholder buy-in that won't happen.
For example: “An AI suggests outsourcing a function currently managed in-house to cut costs 30 percent. You have multi-year vendor contracts with early termination penalties totaling 2 million dollars. How does that change your evaluation?”
5.Identifying hidden operational constraints
AI systems miss constraints that aren't in training data; you need to spot vendor terms, system limitations, and organizational politics that AI analysis might not weight correctly.
For example: “An AI model recommends retraining your team on new sales software and increasing territory size to drive revenue per rep. You know the sales team is burned out and has high turnover. What operational risks would you highlight in your evaluation?”
A task you may get
Write a specific operational problem you have managed or observed, documenting the constraint, the current process with concrete numbers, and a plausible AI-generated solution that is theoretically sound but faces a practical obstacle you would recognize.
How to prepare
- Choose one major operational or strategic project you have led and document it with specific numbers, constraints, and implementation challenges that came up
- Identify where AI approaches to business optimization might miss practical realities in your domain
- Study examples of good and bad KPI definitions from your operating experience and prepare to explain the difference
- Reflect on times approval dynamics, budget freezes, or vendor contracts blocked what would otherwise have been a good idea
The facts
- Pay
- $90–120/hr
- Hours
- Part time, 40 hours a week
- Where
- Remote
- Field
- Business Operations
- Role type
- Talent network
- Posted
- 9/15/2026
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