AI Hiring

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  Scoring strength at each stage

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  Rejection Scoring

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  Example of Rejected Candidate. High score, Biased Probability close to 1

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  Example of Candidate offer. Low Score, Biased probability significantly lower than 1.

Inspiration

As we grow, our applicant pool does as well. Reviewing applicants can be costly if it is taking time from our Engineering Leads. With some simple logistical regression from all of the data in Greenhouse, we can weigh certain words based on how often that word was associated with a rejected or hired candidate.

What it does

We pool together all of a candidate's submission: CV, GitHub (number of stars, repositories, and followers), and LinkedIn profile. Based on all of our previous offers and rejections, we can weigh words in a point system, where words that occur often in strong candidates count for more, and words that occur often in a weak candidate, are negative.

How we built it

"By a simple logistic regression and some NLP" - Alex Tselikov

Challenges we ran into

How to get appropriate data

How to parse CV's that often come in PDF format

How to extract relevant keywords

How to present results

Accomplishments that we're proud of

It works! This point system suggests that we are most successful in hiring quality candidates when they are referred, or met at an event.

What's next for AI Hiring

Using a workable version in daily hiring practices as an augmented hiring tool. A tool like this is unlikely to fully replace human review because of biases the system can learn.

Built With

• api
• csv
• eli5-python-package
• etl
• logistic-regression
• machine-learning
• natural-language-processing