Intro to Uncommon

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.

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Problem

Before I joined, Uncommon was a job board that offered job seekers the opportunity to search, browse, and apply to open positions. But the initial response from early users was not ideal. Most applicants found little or no success after applying to jobs because they never hear back from most recruiters.

We investigated the issue by interviewing and observing recruiters for days and learned a glimpse of the overwhelming responsibility and complexity that inhibited them from finding the right talent.


The tracking process and communication outreach are also strenuous. Most recruiters are responsible for more than ten requisitions on any given day, that is hundreds of applications to sort through per requisition. 


In order to reach as many people as possible, recruiters must advertise extensively on multiple platforms. It is labor-intensive and biased, so the results are often inconsistent. Even with the best intentions, compatible matches are often overlooked. The integration between platforms and their internal applicant tracking systems can also cause unnecessary complications, such as out-of-sync, duplicate data, incompatibility due to legacy systems, etc.


There is a pessimistic sentiment towards artificial intelligence-powered tools because results seem to come from a “black box”, are inconsistent, and are often not justified. Furthermore, we discovered some internal concerns regarding job security since artificial intelligence may replace them someday.


How might we help recruiters to shorten time-to-fill and reduce the cost-per-hire?

Solution

Introducing Uncommon, the qualification-based talent marketplace that demystifies the matching algorithm with human-interpretable matching mechanics.


We pivoted from the job jobard to a recruiter platform designed to demystify the matching algorithm by empowering recruiters with full control and transparency. This way, we serve as a tool that complements the recruiter’s skill set, rather than detract from it.

Instead of basic filters, we asked recruiters to enter their search criteria based on unique qualifications filters that nest in the dashboard sidebar. 


As recruiters manipulate each qualification, the side-by-side comparison of matching candidates will update in real-time. The match detail view will provide every match criteria with human-interpretable explanations, so recruiters can justify how and why they selected every candidate.

For passive candidates, we created an Automated Outreach System that allows recruiters to send and track bulk emails on their behalf, with templates that are optimized for response rates, and can be customized with variants. 


To prevent recruiters from having to manage multiple sourcing platforms, we partnered with vendors to create Automatic Bidding Optimization, which helps recruiters programmatically advertise their job listings to source active candidates on more than 13 platforms. 


Using qualifications as the language, we were able to create defensible merits that will help recruiters identify candidates with consistent accuracy, reducing their workload so they can focus on interviews and high-level strategies. Also, by not charging recruiters for bad matches like other sourcing platforms, we will reduce their cost-of-hire from day one.

Results

In February 2018, we launched with $18 million in Series A funding (funded by Spark Capital, Zeev Ventures, and Canaan Partners), received hundreds of users, and partnered with companies such as Lyft, Aflac, Etsy, and others. 

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