What You Might Not Have Realised About the Google Cloud Jobs API

This isn’t a big deal.  It’s HUGE.  While some people are talking at a very high level about the recent release of the Google Cloud Jobs API, most have glossed over [read: missed] what this could mean in relation to the standard of candidate experience, Indeed’s position as the master of SEO for jobs and LinkedIn’s current role as our industry’s 600lb gorilla that has no playmates. And no, this isn’t the same thing as that old Google Job Board discussion – although it’s a tiny bit related, if we’re being honest.
It’s been said for years that Google has, over time, become the first page of our corporate career sites. Rightfully so since the majority of job seekers start their online search for employment the same way that they would search for a plumber. “Hey Google, show me all the plumbers that are in Dallas, TX.” The Google search engine of course does what it’s best at and returns to us a mix of relevant directories and business pages for local toilet repair services – sometimes even seemingly “smart” enough to recommend alternative or more specific search criteria by completing our query before we’ve finished typing it or by asking us if we’d like to see a different set of results that are potentially more accurate based on what it thinks we’re looking for.
Now just think about that for a moment…  (done, yet?)
The Google Cloud Jobs API delivers a machine learning model that uses the job listings many companies are already posting publicly to simply (okay, not “simply”) connect the dots between locations, skills, work preferences and more, all in an effort to deliver the most relevant jobs we might be interested in. This also means that the search giant is helping to tackle things like deciphering poorly written job descriptions and/or normalising job titles. So while Google’s API won’t be re-writing your job descriptions or assigning new vanity titles to those whoppers that your compensation team said were “required,” it will help to connect candidates looking for a “collections call centre job” to your interestingly branded “Revenue Management and Care Representative Service II” role that would otherwise go missed.
[bctt tweet=”Don’t underestimate the potential impact of Google Cloud Jobs API says @therecruiterguy” username=”ATCevent”]
To get this accomplished, Google’s created a proprietary occupation ontology that includes 30 broad job categories like sales, accounting and finance along with their (also proprietary) skills ontology that is made up of an excess of 50,000 hard and soft skills found within those categories. The Cloud Jobs API then uses relational models to identify and group together the most similar skills and occupational families that it feels are appropriate. For you super geeks out there that want more of the details for that breakdown straight from Google:

The mechanics of Cloud Jobs API
At the heart of Cloud Jobs API, there are two main proprietary ontologies that encode knowledge about occupations and skills, as well as relational models between these ontologies.
The occupation ontology, an enhanced evolution of O*NET Standard Occupational Classification, has three layers: The top layer includes approximately 30 broad job categories (e.g., accounting and finance, human resources, restaurant and hospitality). The second layer lists 1,100 occupation families (e.g., emergency registered nurses, foresters, database administrators), and a third layer consists of 250,000 specific occupations (e.g., software engineer, senior software engineer and parking enforcement officer).
The skill ontology defines and organises around 50,000 hard and soft skills with different types of relationships such as is_a, related_to, etc.
Relational models encode the popularity and specificity of each skill for any occupation family and any specific occupation. For example, the relational models that encode JavaScript, HTML, CSS are skills related to occupations Front-end engineer, UI engineer etc. This allows Cloud Jobs API to identify similar occupation families and specific occupations based on the similarity of their skills distributions.

Source: Google Cloud Big Data and Machine Learning Blog

As long as we’re talking numbers let’s focus on the number, seventeen million. Because 17,000,000 is the number of jobs that Google pulled from hundreds of thousands of company websites in order to gather those aforementioned job titles and skills. So while I only know of one company that for years has been chanting about the delivery of an Economic Graph that would “digitally map the global economy to connect talent with opportunity at massive scale,” I don’t know of any organisation that has even a fraction of the demand side of data that Google admittedly possesses and is now putting in to play – for everyone’s benefit.
Additionally, and with time, the landscape changes a bit for those vendors claiming to, for a price, deliver your jobs to the first page of search results on Google. This shift in relevant job visibility could easily impact how a company might currently be investing in Pay-Per-Click strategies and/or deciding if in some instances it’s time to do away with job board aggregators completely.
Is it the death of the job boards?
Nah… Those of us who’ve been in the space for a few decades know that the demise of job boards are predicted on a regular basis. This should not however, take away from the smart moves made by Careerbuilder, DICE and JIBE – the initial testers that have already begun implementing the API and seeing some impressive results. It will be interesting to see how these collaborations will impact our industry, vendors, customers and candidates in the future.
Images: Shutterstock

This article first appeared on CareerXroads.