scaling data science at airbnb

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Scaling Data Science Structure + tooling at Airbnb Elena Grewal / April 9, 2016 / @elenatej

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Scaling Data Science

Structure + tooling at Airbnb

Elena Grewal / April 9, 2016 / @elenatej

I started at Airbnb ~4 years ago

● Ph.D. in Education

● Team grown from 5 to 70

○ Fun fact for this conference:

■ 54% mostly use R, 29% mostly use Python

■ 80% very comfortable with R, 50% with Python

● Company grown from 200 to 2,500

Scaling

● Team growth

● Interview process

● Why diversity

● Using data

○ Top of funnel changes

○ Conversion changes

● Scaling diversity data science

Rapid Data Team Growth

Diversity was important to us, but it wasn’t happening

Women :(

TeamSize

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

We felt good about the data challenges and process

● Popular Quora post

● Process starts being used by other companies (!)

Why act now

● Harder to hire women as ratio declines

● Women could feel excluded on team

● Homogeneity -> narrower range of ideas

We believe in a world where people belong, anywhere.

We started by looking at the data.-

● Manual audit of past apps● EEOC data on inbound

applicants

FUNNEL IMAGE

30% women

No drop off

Drop off

Drop off

We then thought about everything we could possibly do to make a difference

And we did those things.

FUNNEL IMAGE

Lightning talks

Support community

Diversity on multiple dimensions

Encourage applicants

Blog Posts & Interviews

Highlight Women @ Airbnb

Inspire women in data more broadly

Women in data dinners

Create community of senior women in field

Circulates to multiple companies (not just Airbnb)

● Create standard rubric● Binary scoring system● Removed names for a bit● Trained graders● Two graders for each test

to ensure consistency

● “Buddy” coffee chat & support

● 50% women at presentation ● Clearer success criteria

Increase in Female HiresHigh employee satisfaction scores + 100% women belong

Our work is not complete.

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

Scaling our effortsWhat 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

Thank you!