Overview

Role: UX Researcher / UX Designer (Solo)
Timeline: 8 weeks (Fall 2025)
Tools: Figma, Qualtrics, Miro
Platform: Mobile

LinkedIn is one of the most widely used professional networking and job-search platforms, yet many users—especially first-generation college graduates and non-traditional job seekers—struggle to navigate its job-search experience effectively.

This project evaluates LinkedIn’s job-search flow to identify usability and comprehension barriers and propose research-backed design improvements that reduce cognitive overload, improve clarity, and better support underserved job seekers.

Problem Statement

Although LinkedIn aims to simplify the job-search process, first-generation and non-traditional job seekers often feel overwhelmed, confused, and unconfident while using the platform. Inconsistent job postings, irrelevant recommendations, and complex navigation prevent users from forming a reliable mental model of how to search, evaluate, and apply for jobs successfully.

These challenges disproportionately affect users without prior exposure to corporate hiring norms, widening the gap between job seekers and successful outcomes.

Project Goals

The goals of this project were to:

• Identify core usability and navigational challenges within LinkedIn’s job-search experience

• Understand how first-generation and non-traditional users interpret job listings and requirements

• Evaluate the relevance and usefulness of LinkedIn’s job recommendations

• Translate research findings into actionable, user-centered design recommendations

Research Approach

To gain a deeper understanding of both user attitudes and behaviors, I conducted a mixed-methods UX research study. Combining quantitative and qualitative methods allowed me to uncover not only what users struggle with, but why those struggles occur throughout the job-search process.

Methods

• User Surveys: Captured attitudinal data about emotions, confidence levels, and perceived challenges during job searching

• User Interviews: Explored mental models, expectations, and interpretations of job postings

• Usability Testing: Observed real-time behaviors as users searched and evaluated jobs on LinkedIn

Participants

• First-generation college graduates

• First-time job seekers

• Non-traditional job seekers

• Career switchers

• Recent graduates

These participants were selected to reflect users who often lack insider knowledge of hiring systems and professional job-search norms.

Survey Insights (Quantitative Findings)

48% of participants reported irrelevant job recommendations

75% of participants reported feeling overwhelmed when navigating LinkedIn.

Key Findings

Finding 1: Job postings are unclear and inconsistent

Users struggled to interpret job descriptions due to inconsistent formatting, vague requirements, and dense blocks of text. Many were unsure whether they were qualified enough to apply.

“I don’t know if I’m underqualified or if they’re just asking for too much.”

Finding 2: Job recommendations feel irrelevant

Participants reported that recommended jobs often did not align with their skills, experience level, or career goals, leading to frustration and distrust in the platform’s personalization.

Finding 3: Users feel overwhelmed and lack confidence

The combination of information overload, unclear expectations, and distracting UI elements caused users to feel anxious and discouraged during their search.

• 75% of users reported feeling overwhelmed while using LinkedIn’s job-search features

Insights

Insight 1: Users lack a reliable mental model for job searching on LinkedIn

Without a consistent structure or clear signals, users cannot confidently assess job fit or understand next steps.

Insight 2: Personalization does not align with user identity or skills

LinkedIn’s recommendation system fails to account for non-linear career paths and transferable skills common among non-traditional users.

Insight 3: Cognitive overload disrupts focus and reduces job-search success

Excessive information and visual distractions make it difficult for users to stay focused and motivated.

Personas

Two primary personas emerged from the research:

• First-Generation Job Seeker: Motivated but uncertain, lacking confidence in interpreting job requirements

• Career Switcher: Skilled but misrepresented by traditional job titles and keyword-based matching

These personas represent common mental models observed during interviews and surveys, not edge cases.

Task Flow: Applying for a Job on LinkedIn

This task flow illustrates how users currently navigate the LinkedIn job application process and highlights points where confusion, overload, or drop-off commonly occur.

Journey Mapping

Mapping the job-search journey revealed critical breakdowns during:

• Job filtering and sorting

• Interpreting qualifications

• Deciding whether to apply

The most significant drop in confidence occurred when users attempted to determine job fit based on unclear or inflated requirements.

Design Recommendations

Recommendation 1: Simplify job postings to improve comprehension

• Standardized job posting structure

• Highlighted key qualifications and skills

• Reduced text density for scannability

Design Opportunity: Help users quickly understand job expectations and assess fit with confidence.

Recommendation 2: Improve personalization to reflect user skills and goals

• Enhanced skill-based matching

• Improved filtering for experience level and transferable skills

• Clearer explanations for why jobs are recommended

Design Opportunity: Build trust in recommendations by aligning them with user identity.

Recommendation 3: Reduce cognitive overload during job searching

• Simplified navigation hierarchy

• Minimized visual distractions

• Introduced a focused job-search mode

Design Opportunity: Create a calmer, more intentional job-search experience.

Proposed Experience Shift

Before:

• Dense filters

• Ambiguous job requirements

• High cognitive load

After:

• Guided filtering

• Clear skill alignment

• Confidence-driven decision making

Impact

If implemented, these changes would:

• Reduce cognitive overload during job searching

• Increase user confidence in evaluating job opportunities

• Improve accessibility for first-generation and non-traditional job seekers

Reflection & Learnings

This project strengthened my ability to synthesize qualitative research into actionable insights and reinforced the importance of designing for users without insider knowledge of complex systems. It also highlighted how small UX decisions can significantly impact user confidence and equity, while reinforcing the importance of designing for users without assumed prior knowledge.

Next Steps

With more time, I would:

• Validate recommendations through prototype testing

• Collaborate with engineers and stakeholders to assess feasibility

• Explore personalization improvements using user-controlled preference settings