Who Are You? The Art of Cross-Referencing, Github Example

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Yesterday I saw a recruiter’s post on Facebook asking for advice on how to figure out which candidate had texted them when they couldn’t see the name, only the number. (Has it happened to you?)

A recurring problem in sourcing is dealing with insufficient information. You want to reach out only to relevant people, but the data (say, a Github profile) does not have the essential info such as a job title, company, and often, even location. Emailing everyone who lives in Boston and writes in Java will unnecessarily bother senior managers, students, professors, retirees, people who just started a job, etc.

Let’s commit to being spam-free!

The solution to finding the missing qualifying information is cross-referencing – done by you or by software.

The obvious site to look for professional parameters is LinkedIn. And now it has become possible to find everyone by a registered email, no matter which type of account you have – Basic’s fine – by uploading an email list to LinkedIn. The list can be as long as 8-9K records, and you do not even need the first and last names. (Repeat if it didn’t work.)

Here is the process.

  1. Start with a Github search like the above and collect (public) emails. Instant Data Scraper, one of my favorite Chrome extensions, can do it, or use Julia’s Email Extractor along with Autopagerize (all marked with an Asterisk in my tools list.)
  2. There is a wise intermediate step to take before you start the list cross-referencing. In the  Instant Data Scraper search output, you will find locations and bios – you might want to filter by that first. After cross-referencing, the extra data will be “gone” – and some people will not say on LinkedIn profiles things they did in Github bios. (Also – if anyone in the Github list stands out, check them out right away.)
  3. Upload the email addresses to LinkedIn the new way, and you will arrive at a list of profiles with professional info for people for whom you already know a lot:
    • Programming languages they use (or even their numbers of repositories or followers or other Github search filters)
    • Their email address
    • Their LinkedIn profile

The idea of cross-referencing is not new, but the current improved “Contacts” functionality makes the technology available at scale.

Note that their LinkedIn profile may be “shallow” and lack the programming languages listed – but you know that they use, for example, Golang, from Github. You will be surprised how large the percentage of those “LinkedIn-shy” Github users research shows. This means that you will be steps ahead of those colleagues who still only source on LinkedIn: your results will include potential candidates who are “invisible” on LinkedIn!

The advantage of the described method is that it comes at no cost and provides the most up-to-date data. It can also be easily generalized in application to any email list, no matter what the source. (Keep the intermediate “filtering” step in mind for better targeting.)

People aggregators like AmazingHiring do this profile association for users ahead of time, eliminating hands-on cross-referencing and including sites with professional data beyond Github and LinkedIn. (I first wrote about aggregators in 2011, calling them the “Dream Software”).

I am glad to invite you to a webinar on July 14th,

Github – Recruiters’ Paradise

sponsored by AmazingHiring, where we’ll go over this and other endless Github sourcing possibilities.

[Edited: if you missed it, here is the recording: https://www.crowdcast.io/e/github-technical-recruiters-paradise.]

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  1. Pingback: The Future of Sourcing Is Technical (Scraping and Automation) | Boolean Strings

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