Stanford University

Position Title/Short Description

Full Text of JOE Listing:

The Golub Capital Social Impact Lab (GC Lab) has a 2-year post-doctoral fellowship opening working with The Economics of Technology Professor Susan Athey and other faculty affiliates of the GC Lab to responsibly and equitably use technology to improve people’s lives at scale.

The GC Lab aims to increase the effectiveness of social impact organizations through the use of digital technology, particularly in the domains of education, charitable giving, health, government services, workers and career transitions, and financial health. We collaborate with a broad range of non-profit, non-governmental, and for-profit organizations to research and achieve social impact goals. The GC Lab is housed in the Graduate School of Business (GSB) at Stanford University and contributes to the Business and Beneficial Technology pillar of the Business, Government, and Society Initiative therein.

We are seeking to hire an applied statistician with a background in econometrics or causal inference, preferably with a focus on economics or other social science applications. The ideal candidate will have a strong computational and data science background, and an interest in social science research to improve people’s lives through beneficial technology. Excellent candidates with other specialties or backgrounds related to the lab’s work will also be considered.

There are a number of projects to which the postdoctoral fellow may be matched based on interest, most of which involve collaborating with a social impact team or organization in the tech sector. The projects span the domains of education, labor, and personal finance. Examples of our related research include developing new methods for modeling worker transitions through their career to better understand the gender wage gap, developing recommendation systems for educational applications, or writing personalized stories in an English-language learning app for children.

The position involves:
- Developing and working on research projects using AI, machine learning, and data science;
- Designing and implementing randomized experiments to test the impact of new algorithms or tech-based interventions;
- Interfacing with tech firm partners; and
- Supervising research assistants at various levels of seniority.

The ideal candidate is either preparing for an academic position in a field closely aligned with the lab, for which collaboration on the lab’s projects would serve as strong preparation, or an industry position, (i.e., in a technology company). This position does not incorporate independent research by the fellow outside the scope of the lab; any independent research would be conducted outside of regular work hours and should be managed so as to not present a conflict of commitment to the lab.

Depending on the fellow’s skills/interests, the fellowship will allow the opportunity to: use and develop cutting-edge methodology for working with large data sets, including machine learning and causal inference tools; develop expertise in managing large-scale empirical projects with code bases written by teams; create novel experimental designs, including adaptive and dynamic treatment regimes, bandits, and contextual bandits; run experiments in collaboration with technology firms or on tech firm platforms; and/or develop coding expertise for publicly released software.

Desirable skills and experience include:
- PhD (completed by start of employment) in Economics, Operations Research, Business or Management disciplines, or a related field in Social Science;
- Strong background in econometrics, machine learning, natural language processing, computer vision, or related areas;
- Excellent coding skills and experience with statistical software in multiple languages (R, Python are used regularly);
- Excellent organizational skills, including the ability to set goals, track progress, and prioritize to meet deadlines;
- Strong communication skills;
- Experience with econometrics and/or causal inference; large datasets; cloud computing services; GitHub; experiments; interdisciplinary research; working in collaborative teams; and managing research assistants.