AI Knows Best
Designing a Job Board for an AI enabled Aptitude Assessment tool
See MayaMaya
Designed in Fall 2023
Context: The $1.1 Trillion Problem.
As AI transforms the workplace and the shelf-life of technical skills shrinks, soft skills are becoming increasingly valuable. Yet, students are asked to make career choices early—often before they fully understand their strengths. The result? Costly career pivots and mismatched job placements, which contribute to a workforce misaligned with its roles.
As AI transforms the workplace and the shelf-life of technical skills shrinks, soft skills are becoming increasingly valuable. Yet, students are asked to make career choices early, often before they fully understand their strengths. The result? Costly career pivots and mismatched job placements, which contribute to a workforce misaligned with its roles.
According to Forbes, this growing skill gap may cost the global economy as much as $1.1 trillion.
According to Forbes, this growing skill gap may cost the global economy as much as $1.1 trillion.
Catenate Soft Skills Flower Chart
Catenate Corp’s product suite addresses this by understanding users’ soft skills, and helping them discover roles they may be suited for using their patented, AI enabled assessment system. (See MayaMaya for professionals, and MiraMira for students)
Catenate Corp’s product suite addresses this by understanding users’ soft skills, and helping them discover roles they may be suited for using their patented, AI enabled assessment system. (See MayaMaya for professionals, and MiraMira for students)
Catenate Corp’s product suite tackles this challenge by helping users identify their soft skills and uncover roles where those traits are valued. Their patented AI-powered assessment system powers tools like MayaMaya (for professionals) and MiraMira (for students), guiding users toward more aligned, fulfilling careers.
Catenate Corp’s product suite tackles this challenge by helping users identify their soft skills and uncover roles where those traits are valued. Their patented AI-powered assessment system powers tools like MayaMaya (for professionals) and MiraMira (for students), guiding users toward more aligned, fulfilling careers.
Matching Soft Skills to Roles
Duration: 2 Months
Team: 1 Designer (me), Lead Developer (Sameer Ranjan), CMO (Shiva Vishwanathan), CEO (Karthik Vishwanathan)
MayaMaya began as a tool that gamified soft skill development. After completing quizzes, users were matched with resources to strengthen their skills. I joined to help expand this vision: designing a job board that connects users to roles aligned with their skill profiles.
MayaMaya began as a tool that gamified soft skill development. After completing quizzes, users were matched with resources to strengthen their skills. I joined to help expand this vision: designing a job board that connects users to roles aligned with their skill profiles.
When I joined in Fall 2023, the tech job market was still recovering from the pandemic. Stress among job seekers was high, and many candidates described a “spray and pray” approach to applications—applying to as many roles as possible without much guidance or clarity.
When I joined in Fall 2023, the tech job market was still recovering from the pandemic. Stress among job seekers was high, and many candidates described a “spray and pray” approach to applications—applying to as many roles as possible without much guidance or clarity.
In other words, users weren’t actively looking for guidance—they were looking for efficiency. They wanted help navigating a high-volume, often overwhelming process.
In other words, users weren’t actively looking for guidance—they were looking for efficiency. They wanted help navigating a high-volume, often overwhelming process.
To bridge MayaMaya’s mission with user needs, we set out to design a job board that would help applicants easily identify and apply to roles that matched their strengths.
To bridge MayaMaya’s mission with user needs, we set out to design a job board that would help applicants easily identify and apply to roles that matched their strengths.
We synthesized the following key features to accomplish this:
We synthesized the following key features to accomplish this:
Cards: Recommended (left), Applied (right)
A “Best Fit” indicator (before AI integration in job boards made this common place)
A “Quick Apply” that sent tailored applications with a click of a button
A space to monitor the status of job applications that had been sent
Cards: Recommended (left), Applied (right)
Building trust: How well can AI really know me?
As we moved into usability testing our Jobs features, we uncovered a deeper friction point—one that had less to do with buttons or layout, and more to do with belief.
As we moved into usability testing our Jobs features, we uncovered a deeper friction point—one that had less to do with buttons or layout, and more to do with belief.
At this point in the product journey, users weren’t just asking how to use the job board—they were wondering why they should trust it. Could an AI really understand their skills well enough to recommend something as consequential as a career move?
At this point in the product journey, users weren’t just asking how to use the job board—they were wondering why they should trust it. Could an AI really understand their skills well enough to recommend something as consequential as a career move?
User Feedback Highlights
While we focused on soft skills, users still expected job platforms to prioritize technical qualifications. The idea of being matched based on communication or adaptability felt unfamiliar—and often suspicious.
While we focused on soft skills, users still expected job platforms to prioritize technical qualifications. The idea of being matched based on communication or adaptability felt unfamiliar—and often suspicious.
User Feedback during testing
To close that trust gap, we focused on making the system’s reasoning more transparent. We introduced a benchmark view that let users compare their soft skills to those typically needed in a given role.
To close that trust gap, we focused on making the system’s reasoning more transparent. We introduced a benchmark view that let users compare their soft skills to those typically needed in a given role.
Peeking into the Black Box
Instead of just seeing what their fit score was, we showed them why. That shift gave users a sense of agency—they could explore where they stood out, where they had room to grow, and how their strengths mapped to the jobs in front of them.
Instead of just seeing what their fit score was, we showed them why. That shift gave users a sense of agency—they could explore where they stood out, where they had room to grow, and how their strengths mapped to the jobs in front of them.
Job Details Page: Match Breakdown
When providing a Fit recommendation, explaining why a candidate is a match—by breaking down factors like technical skills, core strengths, and soft skills—led to an increase in candidate trust. Users reported higher confidence in their match score when shown the rationale behind it (surveyed at the end of the test).
When providing a Fit recommendation, explaining why a candidate is a match—by breaking down factors like technical skills, core strengths, and soft skills—led to an increase in candidate trust. Users reported higher confidence in their match score when shown the rationale behind it (surveyed at the end of the test).
MayaMaya addresses a high-level challenge: helping people align their sense of purpose with their role in society. Practically, this means matching candidates with jobs. While AI’s ability to assess technical skills is advancing, there’s growing demand for systems that also consider soft skills, education, and aptitude.
MayaMaya addresses a high-level challenge: helping people align their sense of purpose with their role in society. Practically, this means matching candidates with jobs. While AI’s ability to assess technical skills is advancing, there’s growing demand for systems that also consider soft skills, education, and aptitude.
Today’s applicant tracking systems (ATS) typically focus on surface-level criteria—like years of experience or hard skills—when filtering thousands of resumes. MayaMaya’s approach aims to go deeper.
Today’s applicant tracking systems (ATS) typically focus on surface-level criteria—like years of experience or hard skills—when filtering thousands of resumes. MayaMaya’s approach aims to go deeper.
Retrospective: Closing the Loop
By 2025, Gen Z will make up 27% of the workforce. But this generation of talent is entering the market with new challenges: rising mental health concerns, career instability, and constant noise in their decision-making.
By 2025, Gen Z will make up 27% of the workforce. But this generation of talent is entering the market with new challenges: rising mental health concerns, career instability, and constant noise in their decision-making.
In this environment, self-awareness isn’t a luxury—it’s a survival skill.
In this environment, self-awareness isn’t a luxury—it’s a survival skill.
MayaMaya’s personality assessment tool already offers users insight into their strengths. But now, with the addition of MayaMaya Jobs, that awareness becomes actionable. As automation reshapes the job market and soft skills take center stage, companies will need new ways to assess what makes someone a good fit. MayaMaya gives them a framework—and gives users a chance to be seen for more than just a résumé.
MayaMaya’s personality assessment tool already offers users insight into their strengths. But now, with the addition of MayaMaya Jobs, that awareness becomes actionable. As automation reshapes the job market and soft skills take center stage, companies will need new ways to assess what makes someone a good fit. MayaMaya gives them a framework—and gives users a chance to be seen for more than just a résumé.
But the real challenge of this project was that no one had done it yet. Connecting self-knowledge to job opportunity required research, iteration, and a leap of faith.
But the real challenge of this project was that no one had done it yet. Connecting self-knowledge to job opportunity required research, iteration, and a leap of faith.
And that’s what made it worth doing.
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