Anyone who has ever undertaken the challenging and tedious task of writing a job description knows that it’s tempting to imagine the perfect candidate while drafting your posting: a Rhodes scholar, with a Ph.D. in Artificial Intelligence, and 10 years of experience with Tensorflow.
Unfortunately, this “purple squirrel” likely does not exist (for one, Tensorflow was only released 2 years ago!), and asking for those qualifications — or any qualifications far beyond what is realistic — is a recipe for failure. In most cases, however, hiring managers only figure this out after the job is listed, as hiring managers and recruiters have no idea how common different qualifications are in the population of real job seekers, and thus how useful it is to list them in a job description.
Including too many qualifications, or qualifications that are too strict, will result in nobody applying for the listing. With too few or too broad requirements — you’ll have an influx of low-quality applicants, which you’ll then have to weed through to find the few potential matches.
But how do you know when your qualifications are too high or too low?
That’s where the Uncommon Talent Forecaster comes in. The Uncommon Talent Forecaster is the recruiting industry’s first tool that helps you optimize your qualifications so that you can find the right talent in the shortest amount of time.
The tool is easy to use, allowing users to select qualifications and automatically determine the exact number of qualified applicants in their region, all in real-time. For instance, if someone asks for something rare – a PhD in Data Science for example – the forecast drops way down into the red zone, indicating there are too few people meeting your asks to obtain a solid talent pool for that role.
Ask for something commonplace – knowledge of Microsoft Powerpoint for example, and the forecast shows you that there are thousands of matches, meaning you’ll be flooded with applicants unless you raise the bar. Ask for something somewhat selective, like Bachelor’s in Computer Science, and you see that you’ll get a reasonable number of qualified candidates — creating a way to ‘Goldilocks’ your hiring. And if you’re curious to see who those people are that meet your qualifications, you can just click a button to see a few sample resumes of some real people.
Check out the Talent Forecaster in action:
How did we do it? It all starts with data. Uncommon has a database of over 50 million resumes from across all industries, roles, seniority levels, as well as different parts of the U.S. Our proprietary models consumed each of those resumes to understand each person’s skill set, including years of job experience, industry experience, seniority, education background, and hard and soft skills.
Almost magically, as you’re entering your job requirements, we scan through all of those resumes, to figure out how many of the people near you would actually be qualified according to those requirements, and to tell you whether you need to make your qualifications more or less restrictive. And we do it all in less than a second.
Uncommon’s founder Amir Ashkenazi and I are no strangers to predictive analytics. At our last company, Adap.tv, we developed the advertising industry’s first supply forecasting tool to show ad buyers available inventory in a given marketplace. Today, that type of forecasting has become commonplace, with most major ad platforms, including Google AdWords and Facebook Ad Manager, offering such tools.
Now we’re bringing that same ease of use to the recruiting space with the Talent Supply Forecaster.
Have you ever had trouble creating the perfect job description? Tell us what happened in the comments below.