Data Scientist, Analytics - Product Foundation

Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.

As Facebook continues to expand to all corners of the world, having performant apps matters more than ever. The vast majority of our users access Facebook using old devices and unreliable networks, and bad performance leads to less engaged users and a poor user experience. In this role you will be working closely with the team responsible for making sure the Facebook app delivers the best possible performance across iOS, Android and Web platforms. Our team works cross-functionally with product and infrastructure teams across Facebook to improve app start time, scrolling performance as well as reduce crashes and minimize resources consumption. We're looking for a thought leader in this space to help us frame up and prioritize all the performance investments that we're making across the company. This position will directly engage with the CTO for the Facebook app and have the opportunity to directly shape our investments and roadmap for the next 3 years. Some key challenges we're looking to solve in the next year include: How often do users have a "bad experience" while using the apps? How can we quantify this and how does it affect engagement? What sectors of the population are the most sensitive to performance regressions and how should we adapt our strategy as a result? How should we prioritize our efforts across different users and performance metrics to deliver the best possible performing app? This position is based in Menlo Park or Seattle, WA offices.


  • 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
  • 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
  • 7+ years experience doing quantitative analysis
  • BA/BS in Computer Science, Math, Physics, Engineering, Statistics or other technical field
  • Experience in SQL or other programming languages
  • Development experience in any scripting language (PHP, Python, Perl, etc.)
  • Ability to communicate the results of analyses with product and leadership teams to influence the strategy of the product
  • Understanding of statistics (e.g., hypothesis testing, regressions)
  • Experience manipulating data sets through statistical software (ex. R, SAS) or other methods
  • Advanced degrees
  • Experience with distributed computing (Hive/Hadoop)

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