Case Studies Facebook Uses Kaggle to Recruit Top Data Science Talent

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	Facebook Uses Kaggle to Recruit Top Data Science Talent

Facebook Uses Kaggle to Recruit Top Data Science Talent

Technology Category	Analytics & Modeling - Data Mining Analytics & Modeling - Machine Learning
Applicable Industries	Professional Service Software
Applicable Functions	Human Resources Product Research & Development
Services	System Integration Training
Challenge	In today’s competitive hiring environment, identifying and attracting the most qualified candidates is challenging even for top tech companies. Facebook has faced difficulties in finding data scientists with the right expertise and skills. Traditional hiring methods, such as resumes and interviews, often fall short in revealing the true capabilities of candidates. To address this, Facebook began running Kaggle competitions in 2012 as part of its data science recruiting strategy. These competitions are designed to attract a diverse pool of data scientists and test their skills in real-world scenarios. Read More
About Customer	Facebook, a leading global social media and technology company, has been at the forefront of innovation in various fields, including data science. With a vast user base and a plethora of data, Facebook constantly seeks top talent to drive its data-driven initiatives. The company’s HR team collaborates with Kaggle, a platform known for its data science competitions, to identify and recruit the best data scientists from around the world. By leveraging Kaggle’s community, Facebook aims to find candidates who possess not only technical skills but also creativity and tenacity. Read More
Solution	Facebook collaborates with Kaggle to design recruiting competitions tailored to specific job descriptions. These challenges are crafted to test the skills of job seekers and showcase the interesting problems they might tackle if hired. For instance, in its first recruiting challenge, Facebook sought data scientists with expertise in social network data, asking participants to predict missing links in a real-life social network. Another challenge involved making predictions about how a map of the entire Internet changes over time. A third competition focused on mining large text datasets, where participants were given over six million questions from StackExchange to predict keywords automatically. These competitions not only attract a large number of participants but also provide a platform for candidates to demonstrate their technical skills, creativity, and problem-solving abilities. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data Mining

Analytics & Modeling - Machine Learning

Applicable Industries

Professional Service

Software

Applicable Functions

Human Resources

Product Research & Development

Services

System Integration

Training

Challenge

In today’s competitive hiring environment, identifying and attracting the most qualified candidates is challenging even for top tech companies. Facebook has faced difficulties in finding data scientists with the right expertise and skills. Traditional hiring methods, such as resumes and interviews, often fall short in revealing the true capabilities of candidates. To address this, Facebook began running Kaggle competitions in 2012 as part of its data science recruiting strategy. These competitions are designed to attract a diverse pool of data scientists and test their skills in real-world scenarios.

About Customer

Facebook, a leading global social media and technology company, has been at the forefront of innovation in various fields, including data science. With a vast user base and a plethora of data, Facebook constantly seeks top talent to drive its data-driven initiatives. The company’s HR team collaborates with Kaggle, a platform known for its data science competitions, to identify and recruit the best data scientists from around the world. By leveraging Kaggle’s community, Facebook aims to find candidates who possess not only technical skills but also creativity and tenacity.

Solution

Facebook collaborates with Kaggle to design recruiting competitions tailored to specific job descriptions. These challenges are crafted to test the skills of job seekers and showcase the interesting problems they might tackle if hired. For instance, in its first recruiting challenge, Facebook sought data scientists with expertise in social network data, asking participants to predict missing links in a real-life social network. Another challenge involved making predictions about how a map of the entire Internet changes over time. A third competition focused on mining large text datasets, where participants were given over six million questions from StackExchange to predict keywords automatically. These competitions not only attract a large number of participants but also provide a platform for candidates to demonstrate their technical skills, creativity, and problem-solving abilities.

Impact #1	The Kaggle competitions have proven to be a successful recruiting strategy for Facebook, revealing more about candidates than traditional hiring methods.
Impact #2	Top participants demonstrate objective technical skills, tenacity, and creativity, which are crucial for data science roles at Facebook.
Impact #3	After each challenge, winners submit their code and resumes, and many are invited to interview at Facebook.

Impact #1

The Kaggle competitions have proven to be a successful recruiting strategy for Facebook, revealing more about candidates than traditional hiring methods.

Impact #2

Top participants demonstrate objective technical skills, tenacity, and creativity, which are crucial for data science roles at Facebook.

Impact #3

After each challenge, winners submit their code and resumes, and many are invited to interview at Facebook.

Benefit #1	The first recruiting challenge attracted more than 3500 entries in just 35 days.

Benefit #1

The first recruiting challenge attracted more than 3500 entries in just 35 days.

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Overview

Facebook Uses Kaggle to Recruit Top Data Science Talent

Operational Impact

Quantitative Benefit