Start by clarifying that an MVP is a learning vehicle, not a feature-limited product, and that the problems it solves are primarily about validating assumptions with minimal resources. Then structure your answer around the core problem types an MVP addresses: desirability, viability, feasibility, and usability, using concrete examples relevant to Indeed's two-sided marketplace.
Pro tip: Emphasize that the most valuable MVP problems are those that test the riskiest assumptions first, and that an MVP's success is measured by validated learning, not by revenue or scale.
Clarify that an MVP is designed to test hypotheses and learn quickly with minimal investment, not to deliver a full product. This sets the right lens for identifying problems.
Break down problems into four key areas: desirability (do users want it?), viability (can it sustain a business?), feasibility (can we build it?), and usability (can users figure it out?).
Apply these categories to Indeed's domain: job seeker pain points (e.g., resume optimization, interview prep) and employer pain points (e.g., candidate screening, job posting efficiency).
Explain that an MVP should target the assumption that, if wrong, would kill the product. For example, testing whether employers will pay for AI-powered candidate matching.
Provide 1-2 concrete MVP examples, such as a simple landing page to gauge interest in a new job seeker service, or a concierge MVP for employer branding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.