Data Scientist, Analytics
Company Name: Facebook
Location: New York, NY
Date Posted: 08th Apr, 2016
- 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 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
- The Data Scientist Analytics role has 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
- 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 metrics
- Building models of user behaviors for analysis or to power production systems
- Product Leadership
- Influencing product teams through presentation of data-based recommendations
- Communicating state of business, experiment results, etc to product teams
- Spreading best practices to analytics and product teams
- Data Infrastructure
- Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica
- Automating analyses and authoring pipelines via SQL and python based ETL framework
- 2+ years experience doing quantitative analysis.
- BA/BS in Computer Science, Math, Physics, Engineering, Statistics or other technical field. Advanced degrees preferred.
- Experience in SQL or other programming languages.
- Development experience in any scripting language (PHP, Python, Perl, etc.)
- Ability to initiate and drive projects to completion with minimal guidance.
- Ability to communicate the results of analyses in a clear and effective manner with product and leadership teams to influence the overall strategy of the product.
- Basic understanding of statistics (e.g., hypothesis testing, regressions).
- Experience manipulating large data sets through statistical software (ex. R, SAS) or other methods.
- Experience with distributed computing (Hive/Hadoop) a plus.