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Analytics Engineer

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Analytics Engineer Responsibilities

  • Collect, organize, interpret, and summarize statistical data in order to contribute to the continued growth of Meta's infrastructure.
  • Apply expertise 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 situations or 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, Engineering, Industrial Engineering, Operations Research, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and three years of work experience in the job offered or in a computer-related occupation.
  • Requires three years of experience in the following:
  • 1. Performing quantitative analysis including data mining on highly complex data sets
  • 2. Data querying language: SQL
  • 3. Scripting language(s) including Python
  • 4. Applied statistics or experimentation, such as A/B testing, in an industry setting
  • 5. Machine learning techniques
  • 6. ETL (Extract, Transform, Load) processes
  • 7. Relational databases
  • 8. Large-scale data processing infrastructures using distributed systems
  • 9. Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.

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