Data Scientist, Product 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 moderate scope where analysis of situations or data requires a review of a variety of factors.
Exercise judgment within defined procedures and practices to determine appropriate action.
Perform quantitative analysis including data mining on highly complex data sets using data querying languages, such as SQL, scripting languages, such as Python, or statistical or mathematical software, such as R, SAS, or Matlab.
Design, execute, and analyze experiments and generate actionable insights for product teams.
Communicate the results of analyses to product or leadership teams to influence strategy.
Build ETL (Extract, Transform, Load) processes in relational databases and implement dashboards and reports that track key business metrics and provide actionable insights.

Minimum Qualifications

Requires a Bachelor's degree in Statistics, Mathematics, Data Analytics, Computer Science, Engineering, Information Systems, Applied Sciences, or a related field and two years of work experience in the job offered or in a computer-related occupation
Requires two years of experience 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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