Data Scientist

Meta•Menlo Park, CA

12d

About The Position

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.

Requirements

Requires a Bachelor's degree (or foreign equivalent) in Statistics, Mathematics, Business Analytics, or a related field and 12 months of experience in the job offered or a data-science related occupation
Requires 12 months of experience in following:
Machine learning techniques
ETL (Extract, Transform, Load) processes
Relational database (SQL or PLSQL)
Developing in Python
Statistical analysis using R
Large scale data processing infrastructures using distributed systems (Hadoop, Hive, MapReduce, or MPI)
Quantitative analysis techniques: clustering, regression, pattern recognition and descriptive and inferential statistics
and Communicating and presenting results of data analyses

Responsibilities

Take advantage of massive amounts of structured data to understand how our customers interact with our product and service offerings.
Proactively identify opportunities to improve the experience of businesses on the Facebook family of apps using data science.
Lead the design, analysis, and interpretation of projects from data requirement gathering to data processing, modeling, and recommendations.
Partner with cross-functional teams to identify new opportunities requiring the use of modern analytical and modeling techniques.
Design and execute experiments (e.g., A/B testing, multi-armed bandit).
Effectively communicate insights and recommendations to business leads and influence strategic decision-making.
Frequently switch between on-the-ground tactical execution and 30k foot strategy.
Data infrastructure: working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica, and automating analyses and authoring pipelines via SQL and Python based ETL framework.