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 Meta products.


• Apply 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 a Bachelor's degree in Statistics, Mathematics, Data Analytics, Computer Science, Engineering, Information Systems, Applied Sciences, or a related field and 2 years of experience in the job offered or in a related occupation.


• Requires 24 months of experience involving the following:


• 1. Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)


• 2. Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches


• 3. Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for products


• 4. Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends


• 5. Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through insights and recommendations


• 6. Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions and


• 7. Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica, automating analyses and authoring pipelines via SQL and Python based ETL framework