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

• Apply your 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.


• Building 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.

Minimum Qualifications

• Requires a Master's degree in Computer Science, Engineering, Information Systems, Mathematics, Statistics, Data Analytics, Applied Sciences, or a related field. Completion of one graduate-level course, one research project, or one internship involving the following skills:


• 1. Machine learning techniques


• 2. Developing in scripting language: PHP, Python, or Perl


• 3. Statistical analysis using R, MATLAB, SPSS, SAS, or Stata


• 4. Large scale data processing infrastructures using distributed systems (Hadoop, Hive, MapReduce, or MPI)


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