The Death of The Unpaid Internship

Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.

The Death of the Unpaid InternshipModeling Demand for Listings on Internships.comExtract 2015 ConferenceOctober 30, 2015


• Data Scientist at Chegg• BA, UC Berkeley

• Worked in a Business Analyst role for a few years• Wanted to do some more exciting things with data so…

• MS, Northwestern University• Chicago was too cold• Interned at Chegg, tackled some cool optimization problems• Joined fulltime after graduating

About Me

3 Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.3

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• An internship marketplace• Employers post listings• Students create a profile, upload their resume, and apply!

• Currently 100k+ positions from 80k+ companies• Acquired by Chegg in 2014

About Internships.com


• Explore company/listing demographics• Understand what generates demand

(more applies) for a listing• Seasonality?• Industry?• Paid vs Unpaid?• Virtual vs Onsite?

• Help to prioritize future project initiatives for the site

What factors determine the popularity of a listing?


• Model the count of applies per listing!• With Views or Number of Days listed as an offset, this is

equivalent to a regression of the rate applies so…• Is Poisson, but with a bit of overdispersion

• Estimate an extra parameter to scale the variance (Quasipoisson, Negative Binomial, etc.)

Approach


Distribution Across States

Most populousstatescomprise 85% of all listings


• Option 1: Regularize, tune for an additional ridge parameter• Option 2: Build a separate GLM for every State• Option 3: Replace with a population density variable

• Link ZIP codes to Metropolitan Statistical Areas • Bin into buckets according to resulting distribution

• Turns out Option 3 worked best!• Simpler model (reduced to 7 variables), and no loss in goodness of fit (decrease

in AIC)• Possibly the latent variable that many of the State variables were a proxy for

Replacing the State Variable


• Population density matters!– Compared to Virtual listings as a baseline,

internships in locations with density less than 5,000/SqMile received much less applications (by a factor of 0.6 times)

– Conversely listings in highly populated areas received up to 1.7 times more

Some Results

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• Paid vs Unpaid had the largest effect!– Paid internships can expect to receive 2.5 times

more applications than unpaid internships!– So make it explicit that your internships are paid!

Some Results

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• Seasonality matters!– Differences in Supply/Demand during Fall/Winter months

Some Results

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• Nobody wants to work during Christmas…– “Summer” is one of our most frequent search queries

Some Results

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Listings available year-round


• Supply/Demand by listing category

Some Results

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• Modeling (beyond GLMs too)– Better features with a simpler model is sometimes preferable to more features

(potentially noisy or redundant) with a complex model• For Internships.com…

– Reconcile supply/demand discrepancies in seasonality• Email marketing campaigns during seasons with influx of listings

– Major linkages between different listings categories• Recommend relevant listings to majors that students might not necessarily know about• Also better capabilities for discovery of different job categories for majors

Takeaways

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Data & Analytics

The Death of The Unpaid Internship