33% Research Assistant for Fall 2018: Big Data in Business-to-Business Marketing
Summary: We are a team of researchers focused on better understanding how firms and consumers use and respond to marketing appeals. Past and ongoing projects include examining how consumers respond to digital advertising appeals, developing an optimal bidding algorithm for purchasing online display ad impressions, quantifying synergies in online and offline marketing, and developing machine learning techniques to identify critical patterns in unstructured sequences. These projects are generally executed in collaboration with corporate partners, and rely on insights gleaned from their data. Our goal is always to publish this research in top tier academic journals. We are currently seeking a research assistant with advanced knowledge of programming languages (particularly Python and SQL) and statistical methods (e.g., limited dependent variable models, machine learning, etc.) appropriate for manipulating and modelling big data. Primary responsibilities will include cleaning and manipulating large scale data from B2B firm, as well as coding statistical models. However, our hope is that the selected student will assist at a deeper level, including reading related academic works, contributing to manuscripts, and developing novel research questions.
Support: In addition to providing the requisite hardware, software, data, and non-technical guidance, the primary investigators are prepared to offer a 33% Research Assistantship.
Location: While most of the tasks associated with this role can be completed remotely, we are seeking a graduate student at UW-Madison community to ease communication and transfer of resources.
Timeframe: This role will extend throughout the 2018 fall academic term.
Contact Information: For more information or to apply for this opportunity, please contact:
Paul R. Hoban
Assistant Professor of Marketing
Wisconsin School of Business
Interested applicants should include a recent resume, a short description (1 page or less) detailing past work with big data processing via Python and SQL, and the names of three references.