Senior Research Scientist - Adaptive Experimentation
- 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 Research Scientist to join the Adaptive Experimentation group as part of the Core Data Science team. The Adaptive Experimentation group's mission is to improve the way we run experiments and make decisions both within and outside of Facebook. Candidates apply expertise to develop Internet-scale statistical methods, ranging from adaptive design of experiments, to large-scale inference, to using machine learning to identify heterogeneous treatment effects, to high-dimensional causal inference with observational data.
- Develop new methods and work collaboratively with engineers to develop internal and open-source tools.
- Provide technical leadership in the area of experimentation and causal inference.
- PhD in statistics, political science, economics, biostatistics, or related field, and proven experience for applied problems in experimentation and causal inference. Familiarity with the potential outcomes framework.
- At least 4 years of industry research experience with Internet experiments ("A/B tests").
- Proficient in R and experience with Python, as well as query languages such as SQL, Hive, or Impala.
- At least one of the following:
- Experience with 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).
- Experience with bandit optimization, adaptive experimentation.
- Experience with Bayesian, Empirical Bayes, or large-scale inference methods.
- Experience working with product teams to put novel research methods into practice.
- Experience with machine learning frameworks, such as PyTorch or Tensorflow.
- Knowledge of causal graphical models.
Facebook is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Facebook is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at firstname.lastname@example.org.
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