applinity

Data Scientist, Analytics

at Meta

Location

New York, NY

Type

full time

Posted

1 months ago

Tailor your résumé to this role in 30 seconds.

Free account · ATS keyword check · per-job bullet rewrite by Claude.

Tailor my résuméApply on company site

Job description

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click “Apply to Job” online on this web page

Responsibilities

  • Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
  • Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses.
  • Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.
  • Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
  • Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.

Minimum Qualifications

  • Requires a Master's degree in Statistics, Mathematics, Data Analytics, or a related field and 2 years of work experience in the job offered or a data science-related occupation
  • Requires e 24 months of experience involving the following:
  • Machine learning techniques
  • Relational database (SQL or PL*SQL)
  • Developing in Python
  • Quantitative analysis techniques: clustering, regression, pattern recognition, and descriptive and inferential statistics and
  • Communicating and presenting results of data analyses