Facebook Core Data Science, PhD Intern (London 2018) in London, United Kingdom

Intro:

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.

Summary:

Facebook is seeking a Data Science Intern to join our Core Data Science team. Individuals in this role are expected to be comfortable working as a software engineer and a quantitative researcher. The ideal candidate will have a keen interest in the study of an online social network, and a passion for identifying and answering questions that help us build the best products.

Required Skills:

  1. Work closely with a product engineering team to identify and answer important product questions

  2. Answer product questions by using appropriate statistical techniques on available data

  3. Communicate findings to product managers and engineers

  4. Drive the collection of new data and the refinement of existing data sources

  5. Analyze and interpret the results of product experiments

  6. Develop best practices for instrumentation and experimentation and communicate those to product engineering teams

Minimum Qualifications:

  1. Minimum: Pursuing Ph.D. in Computational Social Science, Operations Research, Mathematics, Statistics, Econometrics, Computer Science or related field

  2. Must be returning to school for at least one semester/quarter post internship

  3. Extensive experience solving analytical problems using quantitative approaches

  4. Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources

  5. A strong passion for empirical research and for answering hard questions with data

  6. A flexible analytic approach that allows for results at varying levels of precision

  7. Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner

  8. Fluency with at least one scripting language such as Python or PHP

  9. Familiarity with relational databases and SQL

  10. Expert knowledge of an analysis tool such as R, Matlab, or SAS

  11. Experience working with large data sets, experience working with distributed computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)

Industry: Internet