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 conduct field experiments with organizations working to train and connect workers to high-quality jobs.

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 a social scientist with experience in behavioral or experimental research, as well as a strong computational and data science background. An ideal candidate would also have experience conducting field experiments with external partners, and an interest in social science research related to worker training and the labor market. Excellent candidates with other specialties or backgrounds related to the lab’s work will also be considered.

The fellow would work on projects in collaboration with two organizations that train and connect workers to high-quality jobs, and which have run multiple experiments in the past with the GC Lab. Examples include designing and evaluating a behavioral nudge to induce users to add a credential to their LinkedIn profile that increases the probability of finding a job, and identifying personalized treatment assignment policies in a program evaluation for a mentoring program to help women find tech jobs.

The position involves:
- Developing and working on research projects using machine learning, experimental design, and other causal inference techniques;
- Serving as project manager and collaborator on research projects, as a coauthor on some projects, and as an assisting researcher on others;
- 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 tools of machine learning and causal inference; develop expertise in managing large-scale empirical projects with large code bases written by teams; create novel experimental designs, including adaptive and dynamic treatment regimes, bandits, and contextual bandits; and/or run experiments in collaboration with technology firms or on tech firm platforms.

The strongest applicants will have a variety of skills and preparation, and a strong desire to rapidly fill any skills and experience gaps.

Desirable skills/experience:
- PhD (completed by start of employment) in economics, business or management disciplines, statistics, decision sciences, or other relevant scientific discipline;
- Training in behavioral science research methods;
- Excellent coding skills and experience with statistical software in multiple languages (R, Python);
- Excellent organizational skills, including the ability to set goals, track progress, and prioritize to meet deadlines;
- Strong communication skills;
- Experience with econometrics; large datasets; experiments; GitHub; interdisciplinary research; working in collaborative teams; and managing research assistants.