Why Breezy (and all other ATS’s) should integrate Marlowe

At Relink, we practice what we preach and use our own technology when hiring for new positions. Read on and see how Marlowe could improve any hiring process.

One of our core beliefs at Relink is practicing what we preach; if we don't actively use our products, what hope do we have of other companies using them?

When developing our API’s, we have conducted endless tests on thousands of candidates, scoring them on skill, experience, and education. When we are hiring for new roles, we experience first-hand how time consuming it is to manually assess each candidate that applies.

This is of course where Marlowe comes to the rescue. But – and yes, there is a but – the real benefit comes when Breezy integrates Marlowe and automatically scores each candidate on skills, experiences and educational fit – seamlessly integrated into the platform.

To illustrate how we envision this being integrated in the UX of an ATS, we used our Chrome extension that Øyvind, one of our full-stack developers, built to score candidates from LinkedIn. For a list of candidates that applied to a Data Scientist position with Relink, we found these candidates on LinkedIn and scored them using Marlowe. Based on the results, we created the following mockup that shows a quick overview of our hiring pipeline:

If Breezy had integrated Marlowe, the skill, education and experience values would have been added on the fly as candidates applied or were added to the pipeline. In addition to presenting scores in a list view, Marlowe's results can be presented as a summary of candidate-to-job fit that allows recruiters to make better and faster decisions.

To continue our exercise, we used the scores provided by Marlowe to attach scores to each candidate in Breezy. We scored seven candidates a +2, ten a +1 or a 0, and gave three a -1. See the snapshot of the Breezy candidate overview below.

Next, by reviewing each candidate from their resume and cover letter alone, we made initial decisions to phone screen 10 candidates and reject the remaining candidates. The phone screen group included all candidates scoring +2, two that scored +1, and one that scored 0.

The jury is still out on who we hire. But what we can share is that we have two people going through second interviews and technical challenges, both of which scored +2 based on Marlowe's results.

"The real benefit comes when Breezy integrates Marlowe and automatically scores each candidate on skills, experiences and educational fit – seemlessly integrated into the platform."

What just happened, really?

Marlowe API uses machine learning to understand the match between people and jobs, helping recruiters save time when pre-qualifying candidates.

When you post a job description in your ATS, Marlowe gives you a breakdown of what the position entails and gives you pointers on what to look for and what to avoid.

Once candidates start applying, Marlowe analyzes them and presents you with a score highlighting the degree of fit between candidate and job. All it needs is their cover letters and resumes. Each candidate’s three scores (education, skills, and experience scores) are presented in list-view and it is up to you (the recruiter) to decide how to sort the list on either of those competencies and decide if you want to shortlist or not. Voila!

So, as a customer and a vendor, this is our call out to Breezy! Please have a look at Marlowe API so that we get our candidates scored as they enter our pipelines. Just on these 20 candidates, you could have saved us a days work. Imagine when there are hundreds applying to a single job.

And to all other ATS's, now is a unique opportunity to get ahead of the curve!

Interested in a product briefing?

Get in touch and we will set up at customized introduction to our APIs and what you can do with them.

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