Candidate Data Management & AI Matching System
Candidate Data Management & AI Matching System
The company collected extensive candidate data and application information, but recruiters felt overwhelmed. There was no good way to filter through it all. The platform had grown significantly, but core features like filtering were not enough.
The company collected extensive candidate data and application information, but recruiters felt overwhelmed. There was no good way to filter through it all. The platform had grown significantly, but core features like filtering were not enough.
MY ROLE
End-to-end UX/UI Designer
TEAM
PM, Engineers, Data Scientists
TIMELINE
8 months
MY ROLE
End-to-end UX/UI Designer
TEAM
PM, Engineers, Data Scientists
TIMELINE
8 months
Index
Recruiters struggled to find top candidates in a fragmented, non-responsive interface that lacked essential tools, leaving them overwhelmed and frustrated. This directly impacted the business growth, increasing the churn rate and losing clients' trust. The big question that the company had was:
Recruiters struggled to find top candidates in a fragmented, non-responsive interface that lacked essential tools, leaving them overwhelmed and frustrated. This directly impacted the business growth, increasing the churn rate and losing clients' trust. The big question that the company had was:
How do we address all the issues without overloading the team and bring real value?
How do we address all the issues without overloading the team and bring real value?
IMPACT
IMPACT
Churn reduction by
Churn reduction by
8%
Converted clients
Converted clients
5+
Established AI implementation framework for future platform features
Established AI implementation framework for future platform features
APPROACH
APPROACH
I applied design thinking across three strategic iterations based on the critical issues. For the filter organization and the AI-powered match between candidates and job descriptions, I collaborated with the Data Science team, and to ensure the mobile version was technically viable and consistent with the design system, I worked closely with engineers.
I applied design thinking across three strategic iterations based on the critical issues. For the filter organization and the AI-powered match between candidates and job descriptions, I collaborated with the Data Science team, and to ensure the mobile version was technically viable and consistent with the design system, I worked closely with engineers.
RESEARCH
Benchmarking & discovery
Mapped recruiter pain points and audited existing functionalities.
ITERATION 1
CV AI Automation
Introduced AI-powered CV matching and reorganized the candidates' board.
ITERATION 2
Board Simplification
Removed redundancy, eliminated bias-prone filters and fixed accessibility issues.
ITERATION 3
Mobile Responsiveness
Adapted the board and filter panel for tablet and mobile with thumb-friendly patterns.
EXECUTION
EXECUTION
Iteration 1 CV AI Automation
Iteration 1 CV AI Automation
1
2
3
Added
Added
Column inside the candidates' board with every match level in different colors and icons for better visualization.
Filter section for the CV match to find top candidates.
Accuracy settings to adjust the CV match according to the expected experience, education and skills.
Column inside the candidates' board with every match level in different colors and icons for better visualization.
Filter section for the CV match to find top candidates.
Accuracy settings to adjust the CV match according to the expected experience, education and skills.
Iteration 2 Board Simplification
Iteration 2 Board Simplification
1
2
4
3
5
6
7
Removed
Removed
Duplicated CSV download button (disabled, obsolete).
Bias-prone filters: nationality and search-bar filters like name, email, ID.
Inaccessible sliders replaced entirely to improve accessibility.
Duplicated CSV download button (disabled, obsolete).
Bias-prone filters: nationality and search-bar filters like name, email, ID.
Inaccessible sliders replaced entirely to improve accessibility.
Merged
Merged
The obsolete filter for skills, education & experience was incorporated inside the main filter panel.
6+ bulk action buttons were reduced to 3 primary ones and an overflow menu.
The obsolete filter for skills, education & experience was incorporated inside the main filter panel.
6+ bulk action buttons were reduced to 3 primary ones and an overflow menu.
Simplified
Simplified
Instead of sliders, I used just checkboxes, tabs and inputs for accesibility.
Bulk actions: ordered by priority and frequency of use (move candidate, send email and schedule an interview)
Instead of sliders, I used just checkboxes, tabs and inputs for accesibility.
Bulk actions: ordered by priority and frequency of use (move candidate, send email and schedule an interview)
Iteration 3 Mobile responsiveness
Iteration 3 Mobile responsiveness
OUTCOMES
OUTCOMES
KI 1: AI is powerful for synthesizing complex data, but implementing it reliably requires significant effort and close collaboration with technical specialists.
KI 1: AI is powerful for synthesizing complex data, but implementing it reliably requires significant effort and close collaboration with technical specialists.
KI 2: Mobile-first design is essential, recruiters increasingly work from phones, requiring responsive experiences from day one.
KI 2: Mobile-first design is essential, recruiters increasingly work from phones, requiring responsive experiences from day one.
KI 3: Simplicity beats feature bloat. Adding functionality without considering cognitive load creates friction, not value. Users need tools that feel comfortable, not overwhelming.
KI 3: Simplicity beats feature bloat. Adding functionality without considering cognitive load creates friction, not value. Users need tools that feel comfortable, not overwhelming.
LEARNINGS
LEARNINGS
This project taught me that leading successfully means knowing when to involve specialists. I could lead the project and make design decisions, but the solution's success depended on continuous collaboration with Data Science, Engineering, and Customer Success. Moving forward, I'm more intentional about identifying the right stakeholders early and integrating their expertise throughout the process. not just at handoff.
This project taught me that leading successfully means knowing when to involve specialists. I could lead the project and make design decisions, but the solution's success depended on continuous collaboration with Data Science, Engineering, and Customer Success. Moving forward, I'm more intentional about identifying the right stakeholders early and integrating their expertise throughout the process. not just at handoff.