Can a neural network predict if you’ll get interviewed?

In April I experimented with a neural network that could dream up resumes of imaginary people. I used TensorFlow, the deep learning framework from Google, along with thousands of anonymized CVs. The results were intriguing. It’s time to take this idea one step further.


Suppose we take a large number of candidates that applied to jobs. We put their resumes in two buckets – those that made it to interview, and those that didn’t. We train a neural network on this data. Then we give it a new resume that it’s never seen before. Could it predict whether this new candidate will make it to interview?

The Neural Network

I dusted off deep_orange, the Recurrent Neural Network that I created in the Spring. Rather than looking at the complete resume, I decided to look at each individual bullet-point on a candidate’s resume. Like:

  • Managed a project that moved widgets from A to B
  • Designed new websites that doubled our customers
  • Etc

These bullets typically don’t include specifics like the actual company, just a description of responsibilities. It would be fascinating if deep_orange could make a reasonable prediction based only on a bullet-point of text.

We actually have 140,000 bullet-points like this in untapt. I fed this data into deep_orange and left it running on my MacBook Pro overnight. The fan was still whistling loudly the next morning.


When it finally completed a few hours later, deep_orange was ready to do some predicting. It assumes each bullet-point is taken from a job application for a software developer role in FinTech, which is a typical scenario for the untapt platform.

Let’s start with something extreme:

Screen Shot 2016-06-18 at 9.51.32 PM

Fair enough! Now for something more relevant:

Neural Network response

Seems reasonable. Next I tried a really substantive bullet-point:

Neural Network response

That’s a strong opinion! But deep_orange has some views on technology:

Neural Network response

When we tested deep_orange on about 1,000 bullet-points that it hadn’t seen before, it predicted the correct outcome 87% of the time.

What’s next

We’ll be exploring whether we could incorporate deep learning techniques like deep_orange into untapt to increase our accuracy. We’re also considering whether these techniques could provide instant feedback while a candidate writes their resume.

This has potential to be a powerful addition to our algorithm.

Stay tuned – there’s lots more to come.

In the meantime if you’re interested to see how we are applying machine intelligence to the hiring process, please sign up for untapt, or follow me at @edwarddonner.


Ed is co-founder and CEO of untapt. A FinTech veteran and an Oxford alum, Ed was previously MD at JPMorgan and has held technology positions at IBM and various startups. Coder by day, Ed leads a double life as DJ after work. And if you happen to run into him, ask him how much he loves Seamless (hint: a lot).