Improving the talent pipeline with skills-based search

About the company

A North American  remote startup with employees is building software to automate heavy industry control rooms. They struggled to find talent with highly specialized skills necessary for the industrial automation and control room landscape.

The challenge

The company wanted to build the top of its candidate pipeline with roles you would not find on most job boards. They planned to double in size over the next 2 years and hire for highly specialized roles critical to the business. Given their limited technology stack and small headcount, finding the breadth and depth of candidates for such roles proved difficult.

The recruiting team was still being built, with the head of HR supporting hiring managers in their search for candidates. Like most startups, they used Lever for their ATS, with LinkedIn Recruiter as the engine behind searching and referrals.

Hiring managers relied heavily on their networks to find potential hires, but they’d rather spend their time qualifying candidates and interviewing those who make it through screenings. Building out teams and growing the company was a priority, but not one that hiring managers could accomplish on their own.

The solution

To grow quickly, the company needed a better way to find candidates with highly specialized skills. They needed to grow their pipeline with candidates who were uniquely qualified for complex positions. They chose Findem.

Findem’s 3D data expresses people data and company data over time. A skill or expertise listed on a social profile is validated automatically across additional sources. For example, a candidate lists Python as a skill. They have Github code contributions for Python. They have experience with a preferred technology at a similarly sized company. 

Findem’s platform makes it easy to search for candidates with attributes that connect the dots between a person and a company’s journey. As the economy and the industry change, companies can adjust and respond to micro- and macro-shifts. Toggling one or two attribute fields in the Findem platform improved the breadth of their searches and helped them uncover insights into its process.

Instead of burning the budget outside of their hiring area, they used Findem to focus efforts on potential hires by location. Although this greatly reduced the size of the pool, the platform gave the recruiting team the ability to identify candidates who were more likely to respond to their outreach. 

Next, Findem helped them see that the candidate pool wasn’t as small as the recruiting team had originally thought. The company expanded the search to include candidates with computer science and development experience. By searching beyond job title, they found candidates with skills well-suited to the types of roles they’d hoped to fill.

The outcome

Since implementing Findem, the company extended two offers and hired one candidate. With such granular search precision and the collaborative support of Findem, they cut the recruiting and hiring process time in half.

The company screened twice as many candidates when it began using Findem. The roles they targeted—complex, industry-specific positions—aren’t easy to fill with traditional recruiting and sourcing tools.