Data Scientist, Product

Meta Meta · Big Tech · Burlingame, CA

Meta is seeking a Data Scientist, Product to collect, organize, interpret, and summarize statistical data to contribute to the design and development of Meta products. This role involves applying expertise in quantitative analysis, data mining, and data presentation to understand user interactions, partner with Product and Engineering teams, and inform product decisions. The Data Scientist will perform quantitative analysis on complex datasets, design and analyze experiments, communicate insights to leadership, build ETL processes, and implement dashboards. The role requires a Bachelor's degree in a related field and two years of experience in quantitative analysis, data mining, SQL, Python, statistical software, applied statistics/experimentation, machine learning techniques, ETL, relational databases, large-scale data processing, and quantitative analysis techniques.

What you'd actually do

  1. Collect, organize, interpret, and summarize statistical data in order to contribute to the design and development of Meta products.
  2. 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.
  3. Partner with Product and Engineering teams to solve problems and identify trends and opportunities.
  4. Inform, influence, support, and execute our product decisions and product launches.
  5. 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.

Skills

Required

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

What the JD emphasized

  • quantitative analysis
  • data mining
  • machine learning techniques
  • ETL (Extract, Transform, Load) processes