Data Science Postdoctoral Scholar: Field Experiments and Causal Inference
The School of Information at the University of California, Berkeley welcomes applications for multiple postdoctoral scholars to help build and be an integral part of our new web-based Master of Information and Data Science program. The position will include research collaboration, teaching sections in our online master’s program, and participation in the intellectual community at the School of Information and on the Berkeley campus.
We particularly seek a postdoctoral scholar with teaching and/or research experience in field experiments, causal inference, and applied econometrics. We wish to find a scholar who will continue to conduct empirical research using experiments, and who will teach sections of our online course on this topic.
Successful candidates will have earned a doctoral degree in economics, political science, statistics, or a related field of study. The minimum qualification required to be considered an applicant for the position is a Ph.D. or the completion of all degree requirements except the dissertation. A Ph.D. or equivalent is required by the date of hire.
Additional qualifications include: a superior academic performance and publication record; the ability to be self-directed with broadly-defined limits on assignments; excellent communication skills, both oral and written; and a demonstrated ability to interact efficiently with diverse people in a highly multidisciplinary environment.
The job will require teaching multiple sections of the online course, while engaging in research as a member of a stimulating, interdisciplinary intellectual community at UC Berkeley, including a variety of seminars and potential research collaborators in economics and related fields. The students in the School of Information MIDS program are excellent and rewarding to teach, and the environment is a great place to innovate with new pedagogical technology.
The postdoc's primary teaching responsibility would be to teach 15-person sections of an online course on experiments and causality. Each section involves one weekly 90-minute meeting. The course load is usually two sections per semester, three semesters per year (including summer). Because the course is online, it can be taught from anywhere, allowing flexibility to travel to conferences or to do research in the field. We do expect the successful candidate to take up residence in Berkeley and become part of the intellectual community of the School of Information. We also hope the postdoc will end up participating in seminars in the economics department, and otherwise interacting with Cal economists.
We particularly seek candidates for a start date this summer, to prepare for teaching in advance of the start of the Fall 2017 semester beginning August 30, 2017. Other available start dates are April 15, 2017, to prepare for teaching in the Summer semester beginning May 10, 2017; and December 15, 2017, to prepare for the semester beginning January 4, 2018.
This is a full-time, one- or two-year, renewable, postdoctoral position. Starting salaries are typically in the range of $60,000 to $65,000 per year and commensurate with qualifications and experience.
The postdoc must possess a Ph.D. or foreign equivalent conferred less than five years ago (extenuating circumstances, including health and family care, can justify exceptions to this requirement), may not have more than five years of postdoctoral experience including that from other institutions, nor have previously held a tenure-track professorial appointment (employed as an assistant professor, associate professor, or professor).
In addition to postdocs with experience in field experiments, causal inference, and applied econometrics, there are other postdoc positions open for candidates with teaching and research experience in at least one of the following core areas:
- Storing and retrieving data (Databases)
- Visualizing and communicating data
- Applied machine learning
- Privacy, security, and ethics of data
- Very large scale data mining and analysis
- Research design and data analysis
- Exploring and analyzing data (Applied statistics)
- Natural language processing
- Deep learning
Requirements
Documents
- Cover Letter
- Curriculum Vitae
- Statement of Teaching Interests, Experience, and Approach (Please include which MIDS class(es) you are qualified to teach.)
- Short Statement of Research Interests, Experience, and Approach
- 1–3 Recent Publications (PDF version(s))
- Course evaluations (optional)
- Three letters of recommendation
All letters of reference will be treated as confidential per University of California policy and California State law. Please arrange for letters of recommendation to be uploaded directly by recommenders. Please refer potential referees, including when letters are provided via a third party (i.e., dossier service or career center), to the UC Berkeley statement of confidentiality: http://apo.chance.berkeley.edu/evalltr.html.
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy see: http://policy.ucop.edu/doc/4000376/NondiscrimAffirmAct.
How to Apply
To apply, please submit all materials electronically:
Questions may be sent to drew.paulin@ischool.berkeley.edu.
About The I School
UC Berkeley’s newest school, the School of Information (I School), was created in 1994 to address one of society’s most compelling challenges: the need to organize and make sense of the abundance of information that we can now collect, store, and share without regard for cost or distance. The way we organize, represent, govern, and make sense of this information will shape our ability to achieve public as well as private goals.
The I School educates professionals and scholars to understand the problems and possibilities of information, to develop models of information practice, and to design useful and usable information applications, services, and solutions. This requires insights from diverse fields. Our faculty includes scholars and professionals with deep expertise in information and computer science, social sciences, management, law, design, and policy, as well as related fields.
We offer two professional master’s degrees and an academic doctoral degree. Our on-campus master’s program (MIMS) trains students for careers as information professionals and emphasizes small classes and project-based learning. Our web-based master’s program (MIDS) is the first and only degree available completely online to train data science professionals. Our Ph.D. program equips scholars to contribute to knowledge and to the policies that influence the organization, use, and sharing of information.
Our graduates work at well-known Bay Area companies that include Apple, Google, Facebook, Salesforce, Twitter, and LinkedIn, as well as at nonprofits like Kaiser Permanente and established businesses like Wells Fargo and Chevron. Many of our graduates take advantage of the opportunity to get in on the ground floor to create or work for start-ups.