In a world where finding candidate-fit is a costly rarity, Uncommon is a recruiting marketplace with human-interpretable matching mechanics that accurately matches recruiters with 100% interested and qualified candidates, to help recruiters shorten time-to-fill and reduce cost-per-hire.
As the first and only designer on the team, I worked closely with a product manager to formulate high-level goals and specifications, along with front-end engineers, back-end engineers, and data scientists.
To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. The information in this case study is my own and does not necessarily reflect the views of Uncommon.
It is difficult for recruiters to consistently find candidates who are qualified and interested. They advertise excessively on multiple platforms in order to stay competitive, but the candidates they find are mostly unqualified.
Recruiters tend to skim rather than read through each profile meticulously because it is tedious and time consuming to go through all the profiles, and their automated screener often ignores resumes with incorrect formatting or keywords, thus disregarding potential compatible matches.
For passive candidates, the tracking process and communication outreach are time-consuming. Most recruiters are responsible for more than ten requisitions on any given day, that is hundreds of applications per requisition.
Even with the best intentions, recruiters can fall victim to their experience and learn to judge candidates based on biased criteria, such as race, gender, school, salary, and seniority, and overlook qualified candidates who are compatible in experience and skill.
Recruiters are also concerned that artificial intelligence-powered tools may one day replace them.
How can we help recruiters accurately identify qualified candidates?
Uncommon is a talent marketplace that empowers recruiters to identify qualified candidates using unbiased merit-based matching mechanisms.
Upon discovering that recruiters tend to judge candidates based on their experience and filter out incorrect keywords, causing countless qualified candidates to be overlooked. We decided we needed a better way to filter candidates.
Choosing which filters to include was the first challenge. We decided to eliminate filters like gender, school, salary, and seniority to focus on measurable merits such as skills and experience. We even tried to hide last names in order to prevent bias against race, but recruiters found it to be excessive, because it made retrieving candidates from their ATS (Applicant Tracking System) more difficult. So, we added back the last names.
Working with our data scientists, we made sure users could search beyond having specific skills and also find how many years each candidate may have had the skill. As soon as we nailed this experience, we knew we had something unique, something no other platform at the time could offer.
It was important to not only provide users with match results, but to also make it easy for them to verify each one. To simplify the process, we parsed and transformed each resume into a standardized layout that can not only be read quickly, but can also be viewed side-by-side with their qualification criteria, and the reasons why each result triggered a match, in plain language.
One of the main reasons we did this was to address the fear recruiters have of being replaced by A.I. We wanted to empower them by giving them tools that boost their every input. Their control and ability to evaluate each profile shows that they are a critical part of the process and not just anyone who can operate the tool.
Once recruiters are satisfied with the filters and have verified the results, they can:
1. Push matches directly to their ATS
2. Use Automated Outreach to send bulk messages to passive candidates matches, and wait for interested candidates to show up
3. Use Automated Sourcing to post the job on multiple job boards and wait for active candidates to apply
4. Screen their own list of candidates with our qualification filters by importing their resumes on file
This project was a unique win-win-win opportunity, as it provided an opportunity for recruiters to find more qualified candidates without bias, qualified candidates to land more jobs without getting overlooked, and we were able to create immense value through responsible design.
Hence, this is my proudest project to date.
We shipped in February of 2018 to much press attention, received $18 million in Series A funding (funded by Spark Capital, Zeev Ventures, and Canaan Partners), partnered with companies such as Lyft, Aflac, Etsy, and others, appeared on multiple podcasts, and our customers showered us with compliments and endearing testimonials.
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© 2021 Van Wang • All rights reserved.