Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how Facebook users interact with 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. Work across the following four areas: Product Operations: Forecasting and setting product team goals, designing and evaluating experiments, monitoring key product metrics, understanding root causes of changes in metrics, building and analyzing dashboards and reports, building key data sets to empower operational and exploratory analysis, and evaluating and defining metrics. Exploratory Analysis: proposing what to build in the next roadmap, understanding ecosystems, user behaviors, and long-term trends, identifying new levers to help move key metric, and building models of user behaviors for analysis or to power production systems. Product Leadership: influencing product teams through presentation of databased recommendations, communicating state of business, experiment results, etc. to product teams and spreading best practices to analytics and product teams. 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.

Requires Master's degree in Statistics, Mathematics, Data Analytics, Business Analytics, or a related field. Foreign degree equivalent accepted.

Requires 2 years of work experience in job offered or in a data science-related occupation.

Experience must include 24 months of experience involving following:

1. Machine learning techniques

2. Working with large data sets and network-based data (TCP or HTTP)

3. ETL (Extract, Transform, Load) processes

4. Relational database (SQL or PL*SQL)

5. Developing in Python

6. Statistical analysis using R

7. Large scale data processing infrastructures using distributed systems (Hadoop, Hive, MapReduce, or MPI)

8. Quantitative analysis techniques: clustering, regression, pattern recognition, and descriptive and inferential statistics

9. Communicating and presenting results of data analyses.