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.We are looking for researchers and applied scientists with expertise in large scale graph inferences 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 model selection, identity modeling, geospatial data analysis and statistical computing, you will be empowered to drive impact across all manner of strategic decisions, product, infrastructure and operational use cases at Facebook.RESPONSIBILITIES
- Build pragmatic, scalable, and statistically rigorous solutions to mission critical inferential and decision problems by leveraging or developing state of the art statistical and machine learning 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 1.9 billion users.
- Think creatively, proactively, and futuristically to identify new opportunities within Facebook's long term roadmap for data-scientific contributions.
- MS degree in a quantitative field with 4+ years of relevant experience, or Ph.D. degree in a quantitative field
- Knowledge in machine learning and statistical research covering at least one of the following domains: large scale graph inferences, pattern discovery, relational learning, graphical models, user modeling or spatio-temporal modeling.
- Knowledge in analysis and visualization using off-the-shelf computing software and libraries such as Python or R
- Experience implementing statistical learning algorithms from scratch in lower level languages such as C, C++, Java
- Experience in scalable dataset assembly / data wrangling, such as Hive or Presto
- Experience in implementing algorithms using an iterative graph processing system, such as Apache Giraph