A Machine Learning Model for Salary Estimation

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Some time ago, I tried to scrape every Bay Area profile off LinkedIn until the site blocked my entire office network (Lesson learned: Use a proxy). This was Bad because we were (and still are!) hiring.

The goal was to collect enough data to create a set of classifiers that could estimate a person’s salary from their LinkedIn profile.

LinkedIn profiles were decomposed using Latent Semantic Indexing and mapped to salary estimates based on users’ current job titles. I scraped all the Bay Area salary information from GlassDoor.

Now when we encounter a new profile, we can perform a similarity query, find the nearest matching profiles, and return their salaries.

Previously this was all done using python libraries which made it too slow for public consumption. I finally got around to rewriting it all using Google’s TensorFlow libraries. The only remaining speed bump is the roundabout way I pull a user’s LinkedIn profile.

Here it is, go play with it.

I’ll write more about TensorFlow some other day, but for now I need to spend less time on this and more time on stuff that won’t get me fired.

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Many thanks to Aronima, TingTing, and Wenjie. GlassBowl would not have happened without them.

9 thoughts on “A Machine Learning Model for Salary Estimation

  1. Although the page currently isn’t loading for me right now (Hacker News effect?), assuming your page works the way I think it does/should, I must say that this is a VERY clever honeypot for collecting warm lead LinkedIn profiles. Can’t fool me! 😀

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