For tech companies intent on building a product that spearheads growth or a new direction for the company, they need to allocate a significant portion of resources to R&D.  Finding people who’ve done similar work before is key to faster evolution. Certain attributes of tech talent – like being an open source contributor or competitive coder – represent exactly the type of person companies need to meet their goals. 

Interviewing people with specific career experience like this cuts down on technical assessment time.  Many companies today utilize extensive technical assessments to ensure that they only hire qualified coders.  However, it’s much more efficient to assess these real world, tangible contributions as evidence of technical prowess.  Hiring teams know that someone with strong open-source contributions or competitive coding accolades can code before they even introduce them to the hiring manager.

But how do you source people with these attributes? The short answer; you need the right search technology. 

Characteristics of top open source contributors

A variety of characteristics can point to a person’s skills as a competitive coder or open-source contributor.  For example, here are some trackable markers that point to these attributes:

  • Lines of code on GitHub
  • More than [x] followers on GitHub 
  • Having repos more than [x] forks
  • Having repos more than [x] stars 
  • Activity in Stack Overflow
  • Active member of a known Open Source Project on GH (Apache, Kafa etc.)

    The challenge of sourcing for this manually

    A quick walk-through of the manual sourcing process for these attributes of top coders shows why it’s not feasible on a larger scale.  First, you’d have to search an online database like LinkedIn for someone with the same or a similar title as the position you’re looking for.  Then you’d review their profile and connected GitHub profile, if they have one. 

    It’s not possible to surface candidates based on followers, repo forks or stars. So, you’d have to scroll manually through their contributions on GitHub, identify the relevant technologies they’re familiar with, assess how valuable their work is, and much more.  To further confirm that they’re exceptional coders, you could also review their activity in Stack Overflow and other repositories.   

    All of that is just to understand one candidate’s technical experience.  When you extrapolate this out to multiple candidates, it becomes clear how tedious and time-consuming this process would be for your hiring team.  It’s simply not worth the investment of time or resources when recruiting efforts should be focused on talking with and closing great candidates.  

    How you can source using attribute-based search

    The majority of recruiting problems are ultimately data problems. The struggle to find the right technical talent is often down to a lack of specificity in the talent search and the fact that keywords and boolean strings aren’t a reliable or comprehensive way of building a pipeline of highly targeted candidates.

    Attribute-based search solves this by harnessing all publicly available people data to zero-in on candidates based on millions of ‘attributes’ for every person. Attributes can be tangible, such as verifying if someone is an ‘open source contributor’ or ‘competitive coder’, as well as intangible, like ‘exceptional problem-solving skills’. 

    Using attribute-based search, to find the best open source contributors is easy. Contribution-related attributes, likely in combination with many others, simply need to be selected as part of the talent search. From there, all data sources about companies and profiles are mapped automatically to each other and a list of candidates who possess the desired traits is generated in seconds. This process can pre-identify GitHub, Stack Overflow, and other open-source protocols to gain a complete overview of a candidate’s followers, contributions, forks, stars, and more. The results are triangulated and verified across multiple data sources, eliminating the possibility of human error and bias and leading to highly precise matches. 

    Any number of attributes can go into the start of each talent search, helping companies to find their ideal candidates faster than ever before.  

    A paradigm shift in people search…

    Too much tech recruiting time is spent today on manual sourcing efforts that under-deliver on high-quality pipeline. Using traditional searching methods, it’s simply not possible to guarantee that candidates will possess the attributes or abilities needed for success in the role. Precise candidate searching based on real-world attributes removes the guesswork and increases the efficiency of the recruiting process by giving you a pipeline of high-quality matches at your fingertips.

    Sourcing this way frees up your recruiting team to spend all their time on the phone, closing candidates. Added benefits are the reduction of bias that unavoidably comes into more manual sourcing methods and avoiding wasted phone screens and technical assessments to rule out the wrong candidates. 

    The hunting and pecking methods tied to traditional search are a thing of the past. Ultimately, attribute-based people search gives any talent team a competitive advantage.

    Want to learn more about how to hire for open source experience and other attributes using Findem? Request a demo today.