Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click "Apply to Job" online on this web page.

Data Scientist, Product Responsibilities

• Collect, organize, interpret, and summarize statistical data in order to contribute to the design and development of Meta products.


• Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products.


• Partner with Product and Engineering teams to solve problems and identify trends and opportunities.


• Inform, influence, support, and execute our product decisions and product launches.


• May be assigned projects in various areas including, but not limited to, product operations, exploratory analysis, product influence, and data infrastructure.


• Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.


• Demonstrate good judgment in selecting methods and techniques for obtaining solutions.


• Understand the impact of product reliability issues on the advertisers.


• Identify opportunities to improve the quality of Meta's advertising platform.


• Conduct analysis and communicate insights to drive action for engineering teams that own the platform.

Minimum Qualifications

• Requires a Master's degree in Computer Science, Engineering, Information Systems, Business Analytics, Analytics, Mathematics, Physics, Applied Sciences, or a related field.


• Requires completion of a university-level course, research project, internship, or thesis, in the following:


• 1. Performing quantitative analysis including data mining on highly complex data sets


• 2. Data querying language: SQL


• 3. Scripting language: Python


• 4. Statistical or mathematical software including one of the following: R, SAS, or Matlab


• 5. Applied statistics or experimentation, such as A/B testing, in an industry setting


• 6. Machine learning techniques


• 7. ETL (Extract, Transform, Load) processes


• 8. Relational databases


• 9. Large-scale data processing infrastructures using distributed systems, AND


• 10. Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.