This document discusses modeling factors that determine demand for internship listings on Internships.com. It finds that paid internships receive 2.5 times more applications than unpaid ones. Population density also impacts demand, with listings in highly populated areas receiving up to 1.7 times more applications. Seasonality matters too, with demand lowest during December and highest in summer months. The results help identify opportunities to better match supply and demand for internships over time.

1. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
The Death of the Unpaid Internship
Modeling Demand for Listings on Internships.com
Extract 2015 Conference
October 30, 2015

2. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• 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. 3
Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
3
Chegg’s Portfolio of Services
Personalized college
advice, exploration, and
organizational tools
Scholarship search and
financial aid guides
SAT/ACT
test prep 24/7 live
online tutoring
Chegg Study
self-directed
homework help
Life skills,
tips and advice Grad school
Internship search and
career opportunities
Textbooks
and more
We improve outcomes by building 1:1 relationships with students that start
in high school and continue delivering value into their careers

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

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

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

7. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
Distribution Across States
Most
populous
states
comprise
85% of
all listings

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

9. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• 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
9

10. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• 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
10

11. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• Seasonality matters!
– Differences in Supply/Demand during Fall/Winter months
Some Results
11
2 4 6 8 10 12
0.50.60.70.80.91.01.11.2
Listing Creation Month
Effect
Effect on Number of Applications
Relative Volume of Listings

12. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• Nobody wants to work during Christmas…
– “Summer” is one of our most frequent search queries
Some Results
12
2 4 6 8 10 12
0.91.01.11.21.3
Internship Start Date
EffectonNumberofApplies
Listings available
year-round

13. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• Supply/Demand by listing category
Some Results
13
Development
Architecture, Engineering, and Drafting
Arts, Visual and Performing
Business and Finance
Computers and Math
Education, Museum Work, and Library Science
EntertainmentHealth and Medicine
Law and Government
Management
Marketing
Media and Communications
Non-Profit
Office and Administrative Support
Protective Services
Sales
Science
Social Science
Sports and Fitness
Trades and Personal Services
0.02
0.03
0.04
0 50 100 150
Scaled Volume
RateofApplies

14. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
• 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
14

15. Confidential Material – Chegg Inc. © 2005 - 2015. All Rights Reserved.
The Student Hub
TM
Thanks!
derrek@chegg.com