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Facebook is looking for experts in economics and computation (EconCS) to join the Core Data Science team. The team works on a variety of practical research problems in domains such as ads, integrity, social impact, and more. The most qualified applicants will have a passion for bringing their expertise in EconCS into practice, working closely with both researchers in other fields as well as product teams to shape the way people and businesses interact with the platform.

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 rigour and methodological innovation. We are an interdisciplinary team, with expertise in computer science, statistics, machine learning, economics, political science, operations research, and computational social science, among other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address.

Research Scientist, Core Data Science Responsibilities

• Design and implement scalable and scientifically sound solutions to mechanism design and optimization problems using either existing or novel methods on top of Facebook's data infrastructure.

• Work towards long-term ambitious research goals, while identifying intermediate milestones.

• Apply excellent communication skills to develop cross-functional partnerships throughout the company.

• 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

• Ph.D. degree in computer science, economics, computational social science, statistics, and related fields or MS degree with relevant experience.

• Expertise in algorithmic game theory, market design, optimization, and/or machine learning.

• 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

• Publications in relevant technical fields (e.g., EC, WINE, NeurIPS, AAAI, STOC, FOCS, WWW).

• Experience in lower level programming languages such as C++.

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