Product design question

How would you improve LinkedIn's job recommendations for users who are actively exploring a career change?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product sense for a specific, underserved user segment whose signals differ from someone searching within their current field.

Define the segment: users actively exploring a different field or role type than their current job history and profile suggest.
Identify the gap: recommendations likely weight past job titles and skills heavily, which works against someone deliberately changing direction.
Propose an explicit exploring a new direction signal users can set, shifting recommendations to weight transferable skills over exact title match.
Propose supporting content: skill gap guidance showing what is missing for a target role, paired with relevant learning courses.
Define success: application rate on recommended jobs among users who set the career change signal, and eventual role change outcome over time.

Diagnoses the real mismatch: existing logic assumes continuity, which actively works against career changers.
Proposes an explicit user signal to intentionally override history based weighting, rather than inferring intent covertly.
Connects recommendations to actionable skill gap content, adding real value beyond re ranking job postings.

Assuming better algorithmic inference alone solves this without giving users an explicit way to declare intent.
No metric tied to actual longer term career outcomes, only short term click or apply behavior.

How would you avoid over relying on a self reported signal that most users will not bother setting?
How would you measure long term success, given career changes take months?