Research Scientist, Experimental Design and Causal Inference

(Menlo Park, CA)

Facebook's mission is to give people the power to share, and make the world more open and connected. Through our growing family of apps and services, we're building a different kind of company that helps billions of people around the world connect and share what matters most to them. 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 make the world more open and accessible. Connecting the world takes every one of us—and we're just getting started.

Facebook is seeking a Scientist to join the Experimental Design and Causal Inference group as part of the Core Data Science team. The Experiment Design & Causal Inference group's mission is to improve the way we run experiments and make decisions both within and outside of Facebook. Candidates apply expertise to solve novel problems, ranging from adaptive contextual experiments, to using machine learning to identify heterogeneous treatment effects, to high-dimensional causal inference with observational data, and to generalizability.

Responsibilities

  • Scientists will be expected to design and implement novel experiments or quasi-experiments, and develop new methods for causal inference.

Minimum Qualifications

  • PhD in statistics, political science, economics, biostatistics, or related field, and a strong passion for applied problems in causal inference. Candidates should be familiar with the potential outcomes framework.
  • At least 2 years of hands-on research experience with an empirical problems in the social or biomedical sciences, or in the Internet industry.
  • Scientists should be proficient in R.
  • Programming: Proficient in Hack, PHP, JavaScript, or Python. Knowledge of SQL, Hive, or Impala.
  • At least one of the following:
  • Experience with field experiments, experimental design, missing data, survey sampling, and/or panel data.
  • Experience with observational causal inference (e.g., regression adjustment, matching, propensity score stratification), or quasi-experimental methods (e.g., instrumental variables, regression discontinuity, interrupted time series). Knowledge of causal graphical models is a plus.
  • Experience with bandit optimization, adaptive experimentation, and/or Gaussian processes.

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