People Research Scientist, PhD Intern/Co-op - Fall 2018

(Menlo Park, CA - New York, NY)

Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.

At Facebook we pride ourselves on making data-informed decisions. This includes not only decisions we make about our platform, which serves over 2 billion users, but also how we learn more about Facebook's most important asset-our people. The People Analytics team explores complex questions, and generates data-driven insights to attract, develop, motivate, and retain top talent.

Facebook is looking for a People Research Scientist Intern to join the People Analytics team, which designs and builds analytical solutions to help Facebook recruit, grow, and retain talented people. This is a full-time Fall Internship role based in Menlo Park, CA or New York, NY.

The ideal candidate will be a social scientist with expertise in quantitative research methodologies or a quantitative specialist with experience solving social problems. You'll be comfortable improvising and have the ability to work cross-functionally and thrive in a fast-paced organization.

This internship is an opportunity to work on cutting-edge people research problems. You'll also get the chance to work with and learn from great colleagues, who are smart, highly collaborative, and helpful. It's a great work environment with lots of benefits that you'll love.


  • Perform research that advances our understanding of Facebook and its employees
  • Collaborate with others on the People Analytics, Human Resources, and Recruiting teams to translate business problems into analytical questions and relevant insights
  • Use your expertise in statistics, data mining, and/or machine learning to analyze data and practically interpret results
  • Package and share actionable insights on Facebook and its people with teammates and stakeholders
Minimum Qualifications
  • Pursuing a PhD in a people research-focused program with strong quantitative methods coverage (e.g., Industrial-Organizational Psychology, Social/Personality Psychology, Quantitative Psychology, Sociology, Economics, Computational Social Science)
  • Substantive interest in understanding employee motivation, work performance, and organizational behavior
  • Moderate to advanced knowledge of multivariate statistics, modeling, and/or machine learning
  • Ability to appropriately select and apply quantitative analysis techniques to answer complex questions
  • Moderate proficiency in R
Preferred Qualifications
  • Capacity to clearly and concisely explain technical concepts and results to non-technical stakeholders
  • Familiarity with SQL - those not familiar with SQL will need to quickly learn it upon starting
  • Prior internships, work experience, and/or applied analytical/consulting projects outside the classroom
  • Advanced proficiency in R
We're proud to be the #1 Best Place to Work on Glassdoor's Employees' Choice awards. Learn more:

Meet Some of Facebook's Employees

Lauren W.

Global Marketing Lead, Facebook Blueprint

As the marketing lead for Facebook’s Blueprint program, Lauren focuses on building awareness around the program and the adoption of education and training by businesses and advertisers.

Kahina V.

Director of Global Financial Services Partnerships

Kahina and her team help launch new financial products and services on the Facebook platform by acting as the company’s voice into the global finance industry.

Back to top