Uncommon Launches Sourcing Automation Platform

The competition for sourcing automation platforms just heated up with Uncommon releasing its much-anticipated sourcing automation tool. Designed with a “white-box” approach that offers full transparency, Uncommon’s new platform will allow sourcers and recruiters the ability to passively source over 150 million profiles from various sourcers on the web.

Recruiters and sourcers can use the new proprietary candidate matching technology with fully automated screening and messaging tools to activate talent faster. In fact, Uncommon claims to cut sourcing time by as much as 75%.

“I found that 38 of the 50 passive candidates Uncommon delivered were qualified. On LinkedIn,
it takes 200 searches and a full day of sourcing to come up with the same amount of targeted
candidates,” explains Elisa Escobar, a veteran recruiter with experience leading TA teams at
Google, AOL, and Apple.

“We built Uncommon because we saw how manual and time-consuming it is to source qualified
talent today, and as data scientists, we realized that we could build tools to automate a lot of the
busy-work, such as screening profiles and writing personalized outreach messages,” said Teg
Grenager, Uncommon’s CEO. “Adoption of sourcing automation tools like Uncommon over the
next few years will give recruiters a breakthrough in productivity, while at the same time
allowing them to focus on the human connection with the interested and qualified candidates.”

Using the new platform is quite efficient. To start, recruiters and sourcers simply upload a job description. Uncommon automatically structures a list of candidate qualifications. The recruiter or sourcer can further customize the list of qualifications to include a candidate’s job duration, experience level, skill proficiency, and industry related experience. From there, a list of candidates are automatically generated, no Boolean required. Uncommon further explains why each candidate was returned in the search results, adding an extra layer of transparency, or a “white-box” approach, according to Grenager.

Not Another “Black Box” AI system

The magic lies in Uncommon’s proprietary matching system that surpasses Boolean search to reveal a deeper pool of qualified candidates transparently and verifiably. While most AI screening systems on the market apply a “black box” approach, a hidden approach to their algorithm, Uncommon’s AI uses “Human Interpretable Machine Learning.”

Uncommon analyzes the qualifications of the candidates based on their resumes, much the way people do. For example, Uncommon’s machine learning models can look at a resume and know whether the candidate’s degree would be considered an “engineering degree” by a hiring manager, therefore deepening the search and delivering a greater list of candidates.

Instead of marking a candidate qualified without explanation, Uncommon shows a side-by-side comparison of how the candidate stacks up to all of the job requirements asked for by the recruiter in their easy-to-use qualifications editor.

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Uncommon’s new platform goes beyond sourcing and also assists recruiters and sourcers with engaging the candidates that they’ve sourced with integrated email messaging, automated sequencing and bulk outreach.

The new sourcing platform is launching with a FREE model that will allow users to view and message up to ten candidates a week.

Shannon Pritchett is the editor of SourceCon. As a lifelong student in the recruitment industry, Shannon is passionate about improving it. Shannon has a diverse background in training, sourcing, international recruitment, full desk recruiting, coaching, and journalism. Shannon got her start in the recruitment industry at Vanderbilt University and later worked as a Senior Recruiter for Internal Data Resources and Community Health Systems, Social Media Recruitment Ambassador for T-Mobile USA, Director of Recruiting for Moxy, Trainer with AIRS, and last as a Manager of Global Sourcing and Training for ManpowerGroup Solutions RPO.

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