Facebook Research Scientist, Applied Statistics in Menlo Park, California
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 looking for researchers and applied scientists with expertise in statistics to join the Core Data Science team. Core data science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally increase the magnitude of Facebook's successes. By applying your knowledge of such topics as experimental design, causal inference, model selection, identity modeling, geospatial data analysis, social measurement and statistical computing, you will be empowered to drive impact across all manner of product, infrastructure and operational use cases at Facebook.
Build pragmatic, scalable, and statistically rigorous solutions to mission critical inferential and decision problems by leveraging or developing state of the art statistical methodologies on top of Facebook's unparalleled data infrastructure.
Apply excellent communication skills in order to develop cross functional partnerships throughout the company and spread statistical best practices.
Be able to work both independently and collaboratively with other scientists, engineers, designers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value to Facebook's community of over 1.7 billion users.
Think creatively, proactively, and futuristically to identify new opportunities within Facebook's long term roadmap for data-scientific contributions.
MS degree in Statistics or related quantitative field with 4+ years of relevant experience, or Ph.D degree in Statistics or related quantitative field
Knowledge in Statistical research covering at least one of the following domains: causal inference, experimental design, statistical testing, demography, survey adjustment, time series modeling, hierarchical modeling, statistical testing, spatio-temporal modeling, or statistical computing.
Experience in analysis and visualization using off-the-shelf statistical computing software such as the R, Stan, Julia, or python varietals.
Experience implementing statistical learning algorithms from scratch in lower level languages such as C, C++, Java
Experience in scalable dataset assembly / data wrangling
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