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Core Data Science (https://research.fb.com/core-data-science/) is a research and development team, working to improve Facebook’s products, infrastructure, and processes. We generate real-world impact through a combination of scientific rigor and methodological innovation. We are an interdisciplinary team, with expertise in computer science, statistics, machine learning, economics, political science, operations research, and sociology, among many other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address. We are looking for researchers and data scientists to join the team in London and Tel Aviv. We work closely with various product groups throughout Facebook, bringing expertise in modeling, machine learning, statistics, statistical computing, data analysis, algorithms, and social network analysis. By applying your knowledge of such topics you will be empowered to drive impact across all manner of strategic decisions, product, infrastructure and operational use cases at Facebook.

Core Data Science, PhD Intern 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 scientific 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 2 billion users.

• Think creatively, proactively, and futuristically to identify new opportunities within Facebook's long term roadmap for data-scientific contributions.

Minimum Qualifications

• Pursuing Ph.D. in Computational Social Science, Operations Research, Mathematics, Statistics, Econometrics, Computer Science or related field

• Must be returning to school for at least one semester/quarter post internship

• Experience in machine learning, statistical analysis or social network analysis.

• Experience with data analysis using tools such as R or Python, with packages such as NumPy, SciPy, pandas, scikit-learn, tidyverse (dplyr, ggplot2, etc.).

• Ability to initiate and drive research projects to completion with minimal guidance.

• The ability to communicate scientific work in a clear and effective manner.

Preferred Qualifications

• Experience in lower level languages such as C++, Java.

• Experience in scalable dataset assembly / data wrangling, such as Presto, Hive or Spark.

• Publications at top international conferences or scientific journals