SlideShare una empresa de Scribd logo
1 de 28
Descargar para leer sin conexión
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 :(
Team
Size
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 Hires
High 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 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
Thank you!

Más contenido relacionado

La actualidad más candente

10 more lessons learned from building Machine Learning systems
10 more lessons learned from building Machine Learning systems10 more lessons learned from building Machine Learning systems
10 more lessons learned from building Machine Learning systemsXavier Amatriain
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & ReasoningSajid Marwat
 
Building social network with Neo4j and Python
Building social network with Neo4j and PythonBuilding social network with Neo4j and Python
Building social network with Neo4j and PythonAndrii Soldatenko
 
Graph Analytics for big data
Graph Analytics for big dataGraph Analytics for big data
Graph Analytics for big dataSigmoid
 
Lecture 5: 3D User Interfaces for Virtual Reality
Lecture 5: 3D User Interfaces for Virtual RealityLecture 5: 3D User Interfaces for Virtual Reality
Lecture 5: 3D User Interfaces for Virtual RealityMark Billinghurst
 
From Microservices to Service Mesh - devcafe event - July 2018
From Microservices to Service Mesh - devcafe event - July 2018From Microservices to Service Mesh - devcafe event - July 2018
From Microservices to Service Mesh - devcafe event - July 2018Thang Chung
 
I asked ChatGPT for SWOT analysis for Amazon (1).pdf
I asked ChatGPT for SWOT analysis for Amazon (1).pdfI asked ChatGPT for SWOT analysis for Amazon (1).pdf
I asked ChatGPT for SWOT analysis for Amazon (1).pdfGamalSaad12
 
Lecture 2 Presence and Perception
Lecture 2 Presence and PerceptionLecture 2 Presence and Perception
Lecture 2 Presence and PerceptionMark Billinghurst
 
Ai 02 intelligent_agents(1)
Ai 02 intelligent_agents(1)Ai 02 intelligent_agents(1)
Ai 02 intelligent_agents(1)Mohammed Romi
 
A comprehensive guide to prompt engineering.pdf
A comprehensive guide to prompt engineering.pdfA comprehensive guide to prompt engineering.pdf
A comprehensive guide to prompt engineering.pdfAnastasiaSteele10
 
Build Intelligent Fraud Prevention with Machine Learning and Graphs
Build Intelligent Fraud Prevention with Machine Learning and GraphsBuild Intelligent Fraud Prevention with Machine Learning and Graphs
Build Intelligent Fraud Prevention with Machine Learning and GraphsNeo4j
 
Introduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AIIntroduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AISemantic Web Company
 
Unit4: Knowledge Representation
Unit4: Knowledge RepresentationUnit4: Knowledge Representation
Unit4: Knowledge RepresentationTekendra Nath Yogi
 
2022 COMP4010 Lecture3: AR Technology
2022 COMP4010 Lecture3: AR Technology2022 COMP4010 Lecture3: AR Technology
2022 COMP4010 Lecture3: AR TechnologyMark Billinghurst
 

La actualidad más candente (20)

Let's Build a Chatbot!
Let's Build a Chatbot!Let's Build a Chatbot!
Let's Build a Chatbot!
 
10 more lessons learned from building Machine Learning systems
10 more lessons learned from building Machine Learning systems10 more lessons learned from building Machine Learning systems
10 more lessons learned from building Machine Learning systems
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & Reasoning
 
Building social network with Neo4j and Python
Building social network with Neo4j and PythonBuilding social network with Neo4j and Python
Building social network with Neo4j and Python
 
The Dark Web
The Dark WebThe Dark Web
The Dark Web
 
Graph Analytics for big data
Graph Analytics for big dataGraph Analytics for big data
Graph Analytics for big data
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
 
Lecture 5: 3D User Interfaces for Virtual Reality
Lecture 5: 3D User Interfaces for Virtual RealityLecture 5: 3D User Interfaces for Virtual Reality
Lecture 5: 3D User Interfaces for Virtual Reality
 
From Microservices to Service Mesh - devcafe event - July 2018
From Microservices to Service Mesh - devcafe event - July 2018From Microservices to Service Mesh - devcafe event - July 2018
From Microservices to Service Mesh - devcafe event - July 2018
 
I asked ChatGPT for SWOT analysis for Amazon (1).pdf
I asked ChatGPT for SWOT analysis for Amazon (1).pdfI asked ChatGPT for SWOT analysis for Amazon (1).pdf
I asked ChatGPT for SWOT analysis for Amazon (1).pdf
 
Ai 01 introduction
Ai 01 introductionAi 01 introduction
Ai 01 introduction
 
Lecture 2 Presence and Perception
Lecture 2 Presence and PerceptionLecture 2 Presence and Perception
Lecture 2 Presence and Perception
 
Ai 02 intelligent_agents(1)
Ai 02 intelligent_agents(1)Ai 02 intelligent_agents(1)
Ai 02 intelligent_agents(1)
 
types of prompts 1.0.pdf
types of prompts 1.0.pdftypes of prompts 1.0.pdf
types of prompts 1.0.pdf
 
A comprehensive guide to prompt engineering.pdf
A comprehensive guide to prompt engineering.pdfA comprehensive guide to prompt engineering.pdf
A comprehensive guide to prompt engineering.pdf
 
Build Intelligent Fraud Prevention with Machine Learning and Graphs
Build Intelligent Fraud Prevention with Machine Learning and GraphsBuild Intelligent Fraud Prevention with Machine Learning and Graphs
Build Intelligent Fraud Prevention with Machine Learning and Graphs
 
Introduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AIIntroduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AI
 
Unit4: Knowledge Representation
Unit4: Knowledge RepresentationUnit4: Knowledge Representation
Unit4: Knowledge Representation
 
Chapter 1 (final)
Chapter 1 (final)Chapter 1 (final)
Chapter 1 (final)
 
2022 COMP4010 Lecture3: AR Technology
2022 COMP4010 Lecture3: AR Technology2022 COMP4010 Lecture3: AR Technology
2022 COMP4010 Lecture3: AR Technology
 

Destacado

Building Scalable Prediction Services in R
Building Scalable Prediction Services in RBuilding Scalable Prediction Services in R
Building Scalable Prediction Services in RWork-Bench
 
Inside the R Consortium
Inside the R ConsortiumInside the R Consortium
Inside the R ConsortiumWork-Bench
 
Scaling Analysis Responsibly
Scaling Analysis ResponsiblyScaling Analysis Responsibly
Scaling Analysis ResponsiblyWork-Bench
 
R for Everything
R for EverythingR for Everything
R for EverythingWork-Bench
 
High-Performance Python
High-Performance PythonHigh-Performance Python
High-Performance PythonWork-Bench
 
Data Science Challenges in Personal Program Analysis
Data Science Challenges in Personal Program AnalysisData Science Challenges in Personal Program Analysis
Data Science Challenges in Personal Program AnalysisWork-Bench
 
R Packages for Time-Varying Networks and Extremal Dependence
R Packages for Time-Varying Networks and Extremal DependenceR Packages for Time-Varying Networks and Extremal Dependence
R Packages for Time-Varying Networks and Extremal DependenceWork-Bench
 
Analyzing NYC Transit Data
Analyzing NYC Transit DataAnalyzing NYC Transit Data
Analyzing NYC Transit DataWork-Bench
 
Broom: Converting Statistical Models to Tidy Data Frames
Broom: Converting Statistical Models to Tidy Data FramesBroom: Converting Statistical Models to Tidy Data Frames
Broom: Converting Statistical Models to Tidy Data FramesWork-Bench
 
The Political Impact of Social Penumbras
The Political Impact of Social PenumbrasThe Political Impact of Social Penumbras
The Political Impact of Social PenumbrasWork-Bench
 
Reflection on the Data Science Profession in NYC
Reflection on the Data Science Profession in NYCReflection on the Data Science Profession in NYC
Reflection on the Data Science Profession in NYCWork-Bench
 
One Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationOne Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationWork-Bench
 
I Don't Want to Be a Dummy! Encoding Predictors for Trees
I Don't Want to Be a Dummy! Encoding Predictors for TreesI Don't Want to Be a Dummy! Encoding Predictors for Trees
I Don't Want to Be a Dummy! Encoding Predictors for TreesWork-Bench
 
Iterating over statistical models: NCAA tournament edition
Iterating over statistical models: NCAA tournament editionIterating over statistical models: NCAA tournament edition
Iterating over statistical models: NCAA tournament editionWork-Bench
 
Julia + R for Data Science
Julia + R for Data ScienceJulia + R for Data Science
Julia + R for Data ScienceWork-Bench
 
Improving Data Interoperability for Python and R
Improving Data Interoperability for Python and RImproving Data Interoperability for Python and R
Improving Data Interoperability for Python and RWork-Bench
 
Using R at NYT Graphics
Using R at NYT GraphicsUsing R at NYT Graphics
Using R at NYT GraphicsWork-Bench
 
Dr. Datascience or: How I Learned to Stop Munging and Love Tests
Dr. Datascience or: How I Learned to Stop Munging and Love TestsDr. Datascience or: How I Learned to Stop Munging and Love Tests
Dr. Datascience or: How I Learned to Stop Munging and Love TestsWork-Bench
 
What We Learned Building an R-Python Hybrid Predictive Analytics Pipeline
What We Learned Building an R-Python Hybrid Predictive Analytics PipelineWhat We Learned Building an R-Python Hybrid Predictive Analytics Pipeline
What We Learned Building an R-Python Hybrid Predictive Analytics PipelineWork-Bench
 

Destacado (20)

Building Scalable Prediction Services in R
Building Scalable Prediction Services in RBuilding Scalable Prediction Services in R
Building Scalable Prediction Services in R
 
Inside the R Consortium
Inside the R ConsortiumInside the R Consortium
Inside the R Consortium
 
Scaling Analysis Responsibly
Scaling Analysis ResponsiblyScaling Analysis Responsibly
Scaling Analysis Responsibly
 
R for Everything
R for EverythingR for Everything
R for Everything
 
High-Performance Python
High-Performance PythonHigh-Performance Python
High-Performance Python
 
Data Science Challenges in Personal Program Analysis
Data Science Challenges in Personal Program AnalysisData Science Challenges in Personal Program Analysis
Data Science Challenges in Personal Program Analysis
 
R Packages for Time-Varying Networks and Extremal Dependence
R Packages for Time-Varying Networks and Extremal DependenceR Packages for Time-Varying Networks and Extremal Dependence
R Packages for Time-Varying Networks and Extremal Dependence
 
Analyzing NYC Transit Data
Analyzing NYC Transit DataAnalyzing NYC Transit Data
Analyzing NYC Transit Data
 
Broom: Converting Statistical Models to Tidy Data Frames
Broom: Converting Statistical Models to Tidy Data FramesBroom: Converting Statistical Models to Tidy Data Frames
Broom: Converting Statistical Models to Tidy Data Frames
 
The Feels
The FeelsThe Feels
The Feels
 
The Political Impact of Social Penumbras
The Political Impact of Social PenumbrasThe Political Impact of Social Penumbras
The Political Impact of Social Penumbras
 
Reflection on the Data Science Profession in NYC
Reflection on the Data Science Profession in NYCReflection on the Data Science Profession in NYC
Reflection on the Data Science Profession in NYC
 
One Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationOne Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical Computation
 
I Don't Want to Be a Dummy! Encoding Predictors for Trees
I Don't Want to Be a Dummy! Encoding Predictors for TreesI Don't Want to Be a Dummy! Encoding Predictors for Trees
I Don't Want to Be a Dummy! Encoding Predictors for Trees
 
Iterating over statistical models: NCAA tournament edition
Iterating over statistical models: NCAA tournament editionIterating over statistical models: NCAA tournament edition
Iterating over statistical models: NCAA tournament edition
 
Julia + R for Data Science
Julia + R for Data ScienceJulia + R for Data Science
Julia + R for Data Science
 
Improving Data Interoperability for Python and R
Improving Data Interoperability for Python and RImproving Data Interoperability for Python and R
Improving Data Interoperability for Python and R
 
Using R at NYT Graphics
Using R at NYT GraphicsUsing R at NYT Graphics
Using R at NYT Graphics
 
Dr. Datascience or: How I Learned to Stop Munging and Love Tests
Dr. Datascience or: How I Learned to Stop Munging and Love TestsDr. Datascience or: How I Learned to Stop Munging and Love Tests
Dr. Datascience or: How I Learned to Stop Munging and Love Tests
 
What We Learned Building an R-Python Hybrid Predictive Analytics Pipeline
What We Learned Building an R-Python Hybrid Predictive Analytics PipelineWhat We Learned Building an R-Python Hybrid Predictive Analytics Pipeline
What We Learned Building an R-Python Hybrid Predictive Analytics Pipeline
 

Similar a Scaling Data Science at Airbnb

DataEngConf SF16 - Beginning with Ourselves
DataEngConf SF16 - Beginning with OurselvesDataEngConf SF16 - Beginning with Ourselves
DataEngConf SF16 - Beginning with OurselvesHakka Labs
 
DockerCon SF 2015: MomOps in DevOps w/ Mukta Aphale
DockerCon SF 2015: MomOps in DevOps w/ Mukta AphaleDockerCon SF 2015: MomOps in DevOps w/ Mukta Aphale
DockerCon SF 2015: MomOps in DevOps w/ Mukta AphaleDocker, Inc.
 
Institutional team december 2012
Institutional team december 2012Institutional team december 2012
Institutional team december 2012tmeisenbach
 
The Future of Recruiting is Open Source
The Future of Recruiting is Open SourceThe Future of Recruiting is Open Source
The Future of Recruiting is Open SourceRecruitDC
 
Tribe of Five Presentation-Final
Tribe of Five Presentation-FinalTribe of Five Presentation-Final
Tribe of Five Presentation-FinalYuan Chen
 
I Am Data-driven and So Are You
I Am Data-driven and So Are YouI Am Data-driven and So Are You
I Am Data-driven and So Are YouLever Inc.
 
Educate 2017 (Learnosity Developer Conference) Opening Keynote
Educate 2017 (Learnosity Developer Conference) Opening KeynoteEducate 2017 (Learnosity Developer Conference) Opening Keynote
Educate 2017 (Learnosity Developer Conference) Opening KeynoteLearnosity
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising DeckPathFWD
 
DevPeers UX process Phase 1
DevPeers UX process Phase 1DevPeers UX process Phase 1
DevPeers UX process Phase 1Shuyen Phoon
 
The Future of Data in Learning and Development
The Future of Data in Learning and DevelopmentThe Future of Data in Learning and Development
The Future of Data in Learning and DevelopmentNamely
 
Becoming a Salesforce / Data Driven Non-Profit
Becoming a Salesforce / Data Driven Non-ProfitBecoming a Salesforce / Data Driven Non-Profit
Becoming a Salesforce / Data Driven Non-ProfitSarah Parise
 
GovLoop Careers Presentation
GovLoop Careers PresentationGovLoop Careers Presentation
GovLoop Careers PresentationPat Fiorenza
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising DeckTami Forman
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising DeckPathFWD
 
Secretary WorX at Saxion University
Secretary WorX at Saxion UniversitySecretary WorX at Saxion University
Secretary WorX at Saxion UniversitySasja Beerendonk
 
Troubleshooting Recruiting: How To Recruit More Women Into Your Workforce
Troubleshooting Recruiting: How To Recruit More Women Into Your WorkforceTroubleshooting Recruiting: How To Recruit More Women Into Your Workforce
Troubleshooting Recruiting: How To Recruit More Women Into Your WorkforceAggregage
 
A case for digital strategy at UALR
A case for digital strategy at UALRA case for digital strategy at UALR
A case for digital strategy at UALRJennifer Godwin
 

Similar a Scaling Data Science at Airbnb (20)

DataEngConf SF16 - Beginning with Ourselves
DataEngConf SF16 - Beginning with OurselvesDataEngConf SF16 - Beginning with Ourselves
DataEngConf SF16 - Beginning with Ourselves
 
Webinar: Converge 2015 Recap
Webinar: Converge 2015 RecapWebinar: Converge 2015 Recap
Webinar: Converge 2015 Recap
 
DockerCon SF 2015: MomOps in DevOps w/ Mukta Aphale
DockerCon SF 2015: MomOps in DevOps w/ Mukta AphaleDockerCon SF 2015: MomOps in DevOps w/ Mukta Aphale
DockerCon SF 2015: MomOps in DevOps w/ Mukta Aphale
 
MomOps in DevOps
MomOps in DevOpsMomOps in DevOps
MomOps in DevOps
 
Institutional team december 2012
Institutional team december 2012Institutional team december 2012
Institutional team december 2012
 
The Future of Recruiting is Open Source
The Future of Recruiting is Open SourceThe Future of Recruiting is Open Source
The Future of Recruiting is Open Source
 
Tribe of Five Presentation-Final
Tribe of Five Presentation-FinalTribe of Five Presentation-Final
Tribe of Five Presentation-Final
 
I Am Data-driven and So Are You
I Am Data-driven and So Are YouI Am Data-driven and So Are You
I Am Data-driven and So Are You
 
When PPC Met SEO: A Love Story of Clicks and Keywords! - Inna Zeyger, Amsive ...
When PPC Met SEO: A Love Story of Clicks and Keywords! - Inna Zeyger, Amsive ...When PPC Met SEO: A Love Story of Clicks and Keywords! - Inna Zeyger, Amsive ...
When PPC Met SEO: A Love Story of Clicks and Keywords! - Inna Zeyger, Amsive ...
 
Educate 2017 (Learnosity Developer Conference) Opening Keynote
Educate 2017 (Learnosity Developer Conference) Opening KeynoteEducate 2017 (Learnosity Developer Conference) Opening Keynote
Educate 2017 (Learnosity Developer Conference) Opening Keynote
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising Deck
 
DevPeers UX process Phase 1
DevPeers UX process Phase 1DevPeers UX process Phase 1
DevPeers UX process Phase 1
 
The Future of Data in Learning and Development
The Future of Data in Learning and DevelopmentThe Future of Data in Learning and Development
The Future of Data in Learning and Development
 
Becoming a Salesforce / Data Driven Non-Profit
Becoming a Salesforce / Data Driven Non-ProfitBecoming a Salesforce / Data Driven Non-Profit
Becoming a Salesforce / Data Driven Non-Profit
 
GovLoop Careers Presentation
GovLoop Careers PresentationGovLoop Careers Presentation
GovLoop Careers Presentation
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising Deck
 
Path Forward Fundraising Deck
Path Forward Fundraising DeckPath Forward Fundraising Deck
Path Forward Fundraising Deck
 
Secretary WorX at Saxion University
Secretary WorX at Saxion UniversitySecretary WorX at Saxion University
Secretary WorX at Saxion University
 
Troubleshooting Recruiting: How To Recruit More Women Into Your Workforce
Troubleshooting Recruiting: How To Recruit More Women Into Your WorkforceTroubleshooting Recruiting: How To Recruit More Women Into Your Workforce
Troubleshooting Recruiting: How To Recruit More Women Into Your Workforce
 
A case for digital strategy at UALR
A case for digital strategy at UALRA case for digital strategy at UALR
A case for digital strategy at UALR
 

Más de Work-Bench

2017 Enterprise Almanac
2017 Enterprise Almanac2017 Enterprise Almanac
2017 Enterprise AlmanacWork-Bench
 
AI to Enable Next Generation of People Managers
AI to Enable Next Generation of People ManagersAI to Enable Next Generation of People Managers
AI to Enable Next Generation of People ManagersWork-Bench
 
Startup Recruiting Workbook: Sourcing and Interview Process
Startup Recruiting Workbook: Sourcing and Interview ProcessStartup Recruiting Workbook: Sourcing and Interview Process
Startup Recruiting Workbook: Sourcing and Interview ProcessWork-Bench
 
Cloud Native Infrastructure Management Solutions Compared
Cloud Native Infrastructure Management Solutions ComparedCloud Native Infrastructure Management Solutions Compared
Cloud Native Infrastructure Management Solutions ComparedWork-Bench
 
Thinking Small About Big Data
Thinking Small About Big DataThinking Small About Big Data
Thinking Small About Big DataWork-Bench
 
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...Work-Bench
 
Building a Demand Generation Machine at MongoDB
Building a Demand Generation Machine at MongoDBBuilding a Demand Generation Machine at MongoDB
Building a Demand Generation Machine at MongoDBWork-Bench
 
How to Market Your Startup to the Enterprise
How to Market Your Startup to the EnterpriseHow to Market Your Startup to the Enterprise
How to Market Your Startup to the EnterpriseWork-Bench
 
Marketing & Design for the Enterprise
Marketing & Design for the EnterpriseMarketing & Design for the Enterprise
Marketing & Design for the EnterpriseWork-Bench
 
Playing the Marketing Long Game
Playing the Marketing Long GamePlaying the Marketing Long Game
Playing the Marketing Long GameWork-Bench
 

Más de Work-Bench (10)

2017 Enterprise Almanac
2017 Enterprise Almanac2017 Enterprise Almanac
2017 Enterprise Almanac
 
AI to Enable Next Generation of People Managers
AI to Enable Next Generation of People ManagersAI to Enable Next Generation of People Managers
AI to Enable Next Generation of People Managers
 
Startup Recruiting Workbook: Sourcing and Interview Process
Startup Recruiting Workbook: Sourcing and Interview ProcessStartup Recruiting Workbook: Sourcing and Interview Process
Startup Recruiting Workbook: Sourcing and Interview Process
 
Cloud Native Infrastructure Management Solutions Compared
Cloud Native Infrastructure Management Solutions ComparedCloud Native Infrastructure Management Solutions Compared
Cloud Native Infrastructure Management Solutions Compared
 
Thinking Small About Big Data
Thinking Small About Big DataThinking Small About Big Data
Thinking Small About Big Data
 
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...
A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare S...
 
Building a Demand Generation Machine at MongoDB
Building a Demand Generation Machine at MongoDBBuilding a Demand Generation Machine at MongoDB
Building a Demand Generation Machine at MongoDB
 
How to Market Your Startup to the Enterprise
How to Market Your Startup to the EnterpriseHow to Market Your Startup to the Enterprise
How to Market Your Startup to the Enterprise
 
Marketing & Design for the Enterprise
Marketing & Design for the EnterpriseMarketing & Design for the Enterprise
Marketing & Design for the Enterprise
 
Playing the Marketing Long Game
Playing the Marketing Long GamePlaying the Marketing Long Game
Playing the Marketing Long Game
 

Último

Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryJeremy Anderson
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxTasha Penwell
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingsocarem879
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBoston Institute of Analytics
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxHimangsuNath
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksdeepakthakur548787
 
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Milind Agarwal
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data VisualizationKianJazayeri1
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Boston Institute of Analytics
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Boston Institute of Analytics
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...KarteekMane1
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our WorldEduminds Learning
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Seán Kennedy
 

Último (20)

Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
 
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptxThe Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
The Power of Data-Driven Storytelling_ Unveiling the Layers of Insight.pptx
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processing
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdf
 
Insurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis ProjectInsurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis Project
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptx
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing works
 
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data Visualization
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our World
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...
 

Scaling Data Science at Airbnb

  • 1. Scaling Data Science Structure + tooling at Airbnb Elena Grewal / April 9, 2016 / @elenatej
  • 2. 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
  • 3.
  • 4. Scaling ● Team growth ● Interview process ● Why diversity ● Using data ○ Top of funnel changes ○ Conversion changes ● Scaling diversity data science
  • 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. -
  • 12.
  • 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.
  • 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)
  • 20.
  • 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 Female Hires High employee satisfaction scores + 100% women belong
  • 24.
  • 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