Senior Research Professional

Location
Chicago
Posted
Mar 09, 2017
Institution Type
Four-Year Institution
About The Unit: The University of Chicago Booth School of Business is the second-oldest school in the U.S. and second to none when it comes to influencing business education and business practices. Since 1898, the school has produced ideas and leaders that shape the world of business. Their rigorous, discipline-based approach to business education transforms students into confident, effective, respected business leaders prepared to face the toughest challenges. Chicago Booth has the finest set of facilities of any business school in the world. Each of the four campuses (two in Chicago, one in London, and one in Hong Kong) reflects the architectural traditions of its environs while offering a state-of-the-art learning environment. Chicago Booth is proud to claim: - an unmatched faculty. - degree and open enrollment programs offered on three continents. - a global body of nearly 50,000 accomplished alumni. - strong and growing corporate relationships that provide a wealth of lifelong career opportunities. As part of the world-renowned University of Chicago, Chicago Booth shares the University's core values that shape the distinctive intellectual culture. At Booth, they constantly question and test ideas, and seek proof. This extraordinarily effective approach to business leads to new ideas and innovative solutions. Seven of the Booth faculty members have won Nobel Prizes for these ideas - the first business school to achieve this accomplishment. For more information about the University of Chicago Booth School of Business, please visit: http://www.chicagobooth.edu/

Unit Job Summary: The Econometrics and Statistics group at the Booth School of Business of the University of Chicago seeks a postdoctoral Senior Research Professional who will work under the supervision of Dr. Veronika Rockova but will have ample opportunities to collaborate with other members of the group. Conduct innovative statistical research and will work on a range of problems involving statistical modeling, computing, as well as applications. More specific areas include, but are not limited to, Bayesian econometrics, dynamic models, regularization, high-dimensional computing, model selection, Bayesian optimization and causal inference. The Senior Research Professional will have the opportunity to actively participate in the broad interdisciplinary environment at Booth. The position is well suited for strongly research-driven PhD's who seek further academic development. The position is expected to last one year with the possibility of a second year. PRINCIPAL DUTIES AND RESPONSIBILITIES: - Conduct innovative research in statistical methodology/applications/theory. - Contribute to the interdisciplinary environment at Booth through seminar participations and collaborations. - Dissemination of research at internal seminars as well as conferences. - Performing data analysis as well as code development. - Construct data sets in assistance of faculty research projects. - Create models of research findings. - Prepare reports on research progress. - Analyze and organize statistical findings. - Write and edit research documentation.

Unit Education: Ph.D. in statistics, econometrics, biostatistics, or a related area required.

Unit Experience: Mathematical background preferred.

Unit Job Function Competencies: Ability to contribute to code development (such as C/C++) required. Interest in Big Data applications required. Proven data analysis skills are required. Ability to work with both minimal supervision and collaboratively required. Excellent verbal, written, and communication skills, in addition to proven organizational skills, are required. Ability to seek out, coordinate, and appropriately disseminate information required. Ability to tackle multiple tasks concurrently and meet deadlines required. Ability to follow directions and communicate clearly and effectively required. Interest in Bayesian statistics preferred. Skill in parallel processing of large data sets preferred.