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We are looking for research and applied scientists interns to join the Core Data Science team. This group is an interdisciplinary team of researchers and developers working to improve Meta's products, infrastructure and processes by aiming to deliver research and innovation that fundamentally increase the magnitude of Meta's successes. By applying expertise in quantitative methods, we will be empowered to drive impact across all manner of product, infrastructure and operational use cases at the company. Individuals in this role are expected to be comfortable working as a quantitative researcher, data scientist and software engineer. The ideal candidate will have a keen interest in the study of an online social network, and a passion for identifying and answering questions that help us build the best products.The Core Data Science team generates real-world impact through a combination of scientific rigor and methodological innovation. They are an interdisciplinary team, with expertise in computer science, statistics, statistical learning, cryptography, machine learning, economics, political science, operations research, and sociology, among many other fields. This diversity of perspectives enriches the research and expands the scope and scale of projects that this team addresses.Meta is looking for researchers and data scientists to join the team in London and Tel Aviv to work closely with various product groups throughout Meta, 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 Meta.

Research Scientist Intern, Central Science, Strategy and Research 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 Meta's unparalleled data infrastructure.
• Apply good 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 Meta's community of over 3 billion users.
• Think creatively, proactively, and futuristically to identify new opportunities within Meta's long term roadmap for data-scientific contributions.
• Work closely with a product engineering team to identify and answer important product questions using scientific techniques and research methods.
• Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
• Answer product questions and generate insights by using appropriate machine learning, statistical techniques or other relevant scientific modeling approaches on available data.
• Build cross-functional partnerships throughout Meta.
• Communicate best practices in quantitative analysis to cross-functional partners.
• Drive the collection of new data and the refinement of existing data sources.

Minimum Qualifications:

• Pursuing Ph.D. in Computational Social Science, Statistical Learning, Operations Research, Mathematics, Statistics, Econometrics, Computer Science or related field.
• Experience solving analytical problems using quantitative approaches.
• Must be returning to school for at least one semester/quarter post internship.
• Experience in machine learning, statistical analysis or social network analysis.
• Experience communicating quantitative analysis in a clear and effective manner.
• 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.

Preferred Qualifications:

• Knowledge with Python, PHP, or other relevant scripting languages.
• Experience implementing algorithms using C, C++, Java, or other relevant low-level languages.
• Experience analyzing datasets and creating models using R, Python, Julia, MATLAB, or other relevant languages.
• Experience manipulating large datasets using Hive, Presto, or other relevant tools. .
• Publications at top international conferences or scientific journals.