Research Scientist, Experimental Design and Causal Inference
(Menlo Park, CA)
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.
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.
- Scientists will be expected to design and implement novel experiments or quasi-experiments, and develop new methods for causal inference.
- 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.
- 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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