Using data science to improve diversity at Airbnb. Talk by Elena Grewal, Airbnb. To hear about future conferences go to http://dataengconf.com

1. Beginning with ourselves
Using data science to improve
diversity at Airbnb
Elena Grewal / April 7, 2016 / @elenatej

2. I started at Airbnb ~4 years ago
● Ph.D. in Education
● Team grew from 5 to 68
● 2nd woman, only woman on
leadership team

3. 10% -> 47% hired
15% -> 30% team

4. How we got there
● Team growth
● Interview process
● Why diversity
● Using data
○ Top of funnel changes
○ Conversion changes
● Scaling diversity data science

5. Rapid Data Team Growth

6. Diversity was important to us, but it wasn’t happening
Women :(
Team
Size

7. Interview process focused on practical data skills
‘Data challenges’ - Airbnb data + real question
Multi-stage
- Recruiter screen
- Take home data challenge
- Onsite challenge
- 1:1s with hiring manager, business partner, CV

8. We felt good about the data challenges and process
● Popular Quora
post
● Process starts
being used by
other
companies (!)

9. Why act now
● Harder to hire women as ratio declines
● Women could feel excluded on team
● Homogeneity -> narrower range of ideas

10. We believe in a world where
people belong, anywhere.

11. We started by looking at the data.
-

13. ● Manual audit of past apps
● EEOC data on inbound
applicants

14. FUNNEL
IMAGE
30% women
No drop off
Drop off
Drop off

15. We then thought about everything we
could possibly do to make a difference
And we did those things.

16. FUNNEL
IMAGE

17. Lightning talks
Support community
Diversity on multiple
dimensions
Encourage applicants

18. Blog Posts & Interviews
Highlight Women @
Airbnb
Inspire women in
data more broadly

19. Women in data dinners
Create
community of
senior women
in field
Circulates to
multiple
companies
(not just
Airbnb)

21. ● Create standard rubric
● Binary scoring system
● Removed names for a bit
● Trained graders
● Two graders for each test
to ensure consistency

22. ● “Buddy” coffee chat &
support
● 50% women at presentation
● Clearer success criteria

23. Increase in Hires - reverse trend
High employee satisfaction scores + 100% women belong

25. Our work is not complete.

26. Next steps
● Focus on multiple aspects of diversity
○ Apply similar process to thinking about racial diversity
○ Other dimensions as well
○ Continue to improve interview process for all - stay vigilant
● Continue to monitor team culture and belonging of current
employees
● Help the rest of the company and scale the efforts

27. Scaling our efforts
What about the rest of Airbnb?
● Full time data scientist + data engineer to work with our
“People and culture team”
● AWS account held separate from main Airbnb data
● Built tool to request and collect diversity data from referrals
and passively sourced candidates
● Dashboards with diversity data for every team

28. Thank you!