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Analytics Engineer

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Salary

Salary: $180,217/year to $196,900/year + bonus + equity + benefits

Analytics Engineer Responsibilities

  • Collect, organize, interpret, and summarize statistical data in order to contribute to the design and development of Meta products.
  • Apply your 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 diverse scope where analysis of data requires evaluation of identifiable factors.
  • Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
  • Provide data-driven product development strategic guidance by helping the team make informed decisions related to our objectives.
  • Evaluate different investment opportunities for the team to pursue and evaluate the outcome of our different initiatives.
  • Provide operational analytics support to product launches and monitoring product performance.

Minimum Qualifications

  • Requires a Master’s degree in Data Science, Computer Science, Engineering, Information Systems, Mathematics, Statistics, Data Analytics, Applied Sciences, or a related field. Requires completion of a university-level course, research project, internship, or thesis in the following:
  • Performing quantitative analysis including data mining on highly complex data sets
  • Data querying language: SQL
  • Scripting language: 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
  • ETL (Extract, Transform, Load) processes
  • Relational databases
  • Large-scale data processing infrastructures using distributed systems
  • Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.

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