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 Facebook products. Apply your 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 moderate scope where analysis of situations or data requires a review of a variety of factors. Exercise judgment within defined procedures and practices to determine appropriate action.

Minimum Qualifications

• Requires Master's degree in Statistics, Mathematics, Analytics, Computer Science, Engineering, or a related field. Foreign equivalent accepted.


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


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


• 2. Data querying language(s) including SQL


• 3. Scripting language(s) including Python


• 4. Statistical or mathematical software including R, SAS, or Matlab


• 5. Applied statistics or experimentation including 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


• 10. Quantitative analysis techniques, including clustering, regression, pattern recognition, or descriptive and inferential statistics.