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 Responsibilities

• Collect, organize, interpret, and summarize statistical data in order to contribute to the continued growth of company infrastructure.


• Apply experience in quantitative analysis and data mining to improve, and optimize across a variety of domains with an emphasis on long-term and strategic initiatives.


• Work cross-functionally to define priorities and develop business intelligence project roadmaps in synergy with partner teams.


• Drive execution through fast iteration.


• Ensure coordination of projects across related workflows to maximize impact and avoid duplication and overlaps.


• Drive efficient data exploration and modeling.


• Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing statistical and machine learning methodologies.


• Work on problems of moderate scope where analysis of situations or data requires a review of a variety of factors.


• Telecommuting is permitted from anywhere in the U.S.

Minimum Qualifications

• Requires a Bachelor's degree in Statistics, Mathematics, Data Analytics, Computer Science, Engineering, or a related field (or foreign equivalent). Requires also completion of one undergraduate-level course, research project, or internship involving the following:


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


• 2. Data querying languages including SQL Scripting language(s) and Python


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


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


• 5. Machine learning techniques


• 6. Relational databases


• 7. Large-scale data processing infrastructures using distributed systems


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