Data Scientist; Product

Data Scientist, Infrastrategies Responsibilities

• Collect, organize, interpret, and summarize statistical data in order to contribute to the continued growth of Meta's infrastructure.
• Apply experience in quantitative analysis and data mining to improve, optimize, and expand Meta’s infrastructure across a variety of domains with an emphasis on long-term and strategic initiatives.
• Work cross-functionally as a strategic partner to define priorities and develop project roadmaps in synergy with partner teams.
• Build consensus and earn commitment from partners.
• Drive execution through fast iteration.
• Ensure coordination of projects across related workflows to maximize impact and avoid duplication and overlaps.
• Drive efficient data exploration and modeling.
• Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing statistical and machine learning methodologies.
• Generalize methodologies for broader application within and outside domain.
• Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.
• Demonstrate good judgment in selecting methods and techniques for obtaining solutions.

Minimum Qualifications

Requires a Master's degree in Computer Science, Economics, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and one year of work experience in the job offered or in a computer-related occupation.

Requires one year of experience in the following:

• Performing quantitative analysis including data mining on highly complex data sets
• Data querying language: SQL
• Scripting language:
  Python
• Applied statistics or experimentation, such as A/B testing, in an industry setting
• Machine learning techniques
• ETL (Extract, Transform, Load) processes
• Relational databases
• Large-scale data processing infrastructures using distributed systems, AND
• Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.