Build a mobile app that takes a picture of a resume and recommends jobs.

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Anonymous · Accepted Answer

💬 Question: As a PM for LinkedIn, how would you build a resume scanner to help
users discover career pivots?

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⭐️ Answer: I'd start with the core traveler problem—most professionals don't
know what roles match their skills, and exploring options feels risky. A resume
scanner solves that by surfacing adjacent roles they're qualified for, reducing
friction in career exploration.

My approach:

User workflow: Member uploads resume → system extracts skills, experience,
education → surface 3–5 realistic next roles ranked by skill match and market
demand → show salary range, required upskilling, and similar member profiles →
drive job applications or learning content.

MVP scope: Parse resume for skills and seniority level, match against LinkedIn's
job taxonomy and member profiles in target roles, rank by skill overlap and
hiring velocity. Exclude roles requiring certification or relocation unless
explicitly filtered. No AI-generated career advice in V1—just pattern matching.

Key decision: Prioritize skill match over aspirational roles. Members trust
recommendations grounded in what they already have, not what they wish they had.
This drives higher application rates and better placement outcomes.

Measurement: Track resume uploads, pivot role clicks, job applications to
suggested roles, and placement rate 90 days out. Guardrail: ensure suggested
roles have active hiring and realistic salary progression—don't surface dead-end
pivots.

Cross-functional: Partner with Jobs team on role taxonomy, Data Science on skill
extraction accuracy, and Analytics on incrementality testing. The main risk is
resume parsing errors—I'd start with manual validation on a cohort before
scaling.

This connects to my work at CBRE on adoption and user segmentation—I'd segment
by career stage and industry to tailor the experience and measure which groups
benefit most.

Build a mobile app that takes a picture of a resume and recommends jobs.

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