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

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


• Mine massive amounts of data and perform large-scale data analysis to extract useful business insights.


• Develop data pipelines with automated, machine-learning systems that convert noisy core datasets into powerful signals of user behavior.


• Build models of user behaviors for analysis or to power production systems.


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


• Design and implement dashboards and reports that track key business metrics and provide actionable insights.


• Inform, influence, support, and execute our product decisions and product launches by effectively communicating results to cross functional groups.


• Work across areas of product operations, exploratory analysis, product leadership, and data infrastructure to help shape the future of what we build at Meta.


• Telecommuting is permitted from anywhere in the United States.

Minimum Qualifications

• Requires a Master's degree in Statistics, Mathematics, Analytics, Engineering, Computer Science, or a related field. Requires 36 months of experience in the job offered or in a related occupation. Experience must include 36 months involving the following:


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


• 2. Scripting language including Python


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


• 4. Applied statistics or experimentation including A/B testing in an industry setting


• 5. Machine learning techniques


• 6. Quantitative analysis techniques: clustering, regression, pattern recognition, and descriptive and inferential statistics.