Seeking highly skilled PhD researcher with expertise in data science, program evaluation, and policy research. The position is offered are part of an expanding research lab focused on innovation and evaluation in administrative governance. The Post-Graduate Research Fellow will work with multidisciplinary teams to solve high visibility problems in government using data and analysis. 

RegLab is building a scientific evidence base for effective governance through innovation, technology, and evaluation. We focus on demonstrations that illustrate how to use scientific research evidence to improve core government functions and modernize the administrative state. We build and evaluate solutions – including programs and policies already in use for which we have no evidence of effectiveness and those that have not yet been imagined – that range from simple peer review to machine learning as a way to improve decision making in the administrative state. 

Working closely with the Faculty Director and Research Director, the Post-Graduate Research Fellow will be engaged in all aspects of the research process from research design, driving forward analytical methods, deploying data-driven solution, and evaluating interventions. We expect that the Post-Graduate Research Fellow will work on multiple projects and receive co-authorship on these papers.

The Regulation, Evaluation, and Governance Lab at Stanford University (RegLab) is a research lab that uses data science and technology to solve challenges in law, regulatory policy, and governance. 

Our mission is to: 

(1) Modernize the information, technology, and research capacity of the government to shrink the public-private sector learning gap;

(2) Harness rich administrative data that governments routinely collect to design, pilot, and evaluate interventions to improve core functions of government; and 

(3) Evaluate regulatory and policy interventions to empower agency learning in real time.

Projects span various substantive areas and include research on environmental enforcement, food safety, antidiscrimination, access to justice, veterans adjudication, and performance measurement. Our faculty and team of researchers work in a highly collaborative and multidisciplinary environment, and directly engage with local, state and federal governments and policymakers. 

We are looking for candidates who have the ability, experience and energy to be the data and statistical lead for a diverse research program focused on machine learning and policy evaluation. The specific projects will be selected based on the accepted candidate’s skills and interests. This position will offer dedicated mentoring by the PIs and opportunities to work within several different teams of investigators. A committed Fellow will have opportunities to write papers and present the research at national meetings.

The candidate’s field of expertise is open, but extensive training in statistics, data science and / or computer science is highly preferred. An applicant with a working knowledge/mastery of the following programs and languages will be given strong preference: R, Python, and SQL. 

Application Instructions: To be considered, please submit the following items along with your online application:

• Cover Letter

• Resume/CV

• Project and code samples or Github

This is a one-year fixed term position. 

Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce. It welcomes applications from women, members of minority groups, veterans, persons with disabilities, and others who would bring additional dimensions to the university's research and teaching mission.

Stanford Law School seeks to hire the best talent and to promote a safe and secure environment for all members of the university community and its property. To that end, new staff hires must successfully pass a background check prior to starting work at Stanford University.

Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.