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, Analytics 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, Data Science, Information Systems Management, or a related field and 24 months of experience in the job offered or in related occupation. Foreign equivalent accepted. Experience must include 24 months in each of the following skills and technologies:


• 1. Machine learning techniques


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


• 3. Relational database (SQL or PL*SQL)


• 4. Developing in scripting language: Python


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


• 6. Communicating and presenting results of data analyses


• 7. Cloud Technology Services (AWS and Microsoft Azure).