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

• Perform large-scale very-high-complexity data analysis and develop effective statistical models for segmentation, classification, optimization, and time series.
• Understands technical architecture in depth, and influences improvements.
• Design and implement reporting dashboards that track key business metrics and provide actionable insights.
• Identify actionable insights, suggest recommendations, and consistently influences the overall data best practices (e.g.
• analysis, goaling, experimentation) within the area and the direction of the business by effectively communicating results to cross-functional groups.
• Work closely with Product or Engineering and Operations teams to proactively create rule and manage decisions.
• Translates analytical understanding of the area to team priorities and strategic narrative.
• Consistently drives top-line results with analysis, adapting strategy as needed.
• Prioritize leads so that the teams can work on the most valuable cases.
• Improves efficiency and simplicity of shared tools and libraries to help scale the team.
• Demonstrates leadership in analytics org projects.

Minimum Qualifications

• Requires a Master’s degree (or foreign degree equivalent) in Computer Science, Engineering, Information Systems, Analytics, Statistics, Mathematics, Physics, or a related field
• Requires a completion of a graduate-level course, research project, or internship in each of the following:
• Performing quantitative analysis including data mining on highly complex data sets
• Data querying language(s) including SQL
• Scripting language(s) including Python
• statistical or mathematical software including one of the following: R, SAS, or Matlab
• Applied statistics or experimentation, such as A/B testing, in an industry setting
• Machine learning techniques
• Relational databases
• Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics