Data Scientist

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.

Responsibilities

• Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
• Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
• Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
• Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
• Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
• Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.

Minimum Qualifications

• Requires a Master's degree (or foreign degree equivalent) in Engineering, Mathematics, Statistics, Computer Science, Computer Engineering, Management of Technology or a related field and 2 years of experience in the job offered or related occupation in analytics
• Requires 2 years of experience in the following:
• Data querying language SQL
• Scripting languages including: Python or similar scripting language
• Statistical/mathematical packages
• Working with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
• Applied technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for products
• Identifying and measuring success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
• Defining, understanding, and testing opportunities and levers to improve the product, and drive roadmaps through insights and recommendations
• Partnering with product, engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions