Postdoctoral Scholar in Computational Marketing
Advertiser: Economics, Graduate School of Business, Stanford University
Field(s) of specialization: Marketing
Position type(s): Postdoctoral Scholar
Location of job: 655 Knight Way, Stanford, California, 94305, United States (map)
Degree required: Doctorate
Job start date: Flexible
Job duration: Continuing/permanent
Letters of reference required: 2
Application deadline: 10 Sep 2021 (accepting applications)
Posting end date: 10 Sep 2021
Interviews: Interviews will be conducted remotely by video
Ad text:

Post-Doc, Computational Marketing

As a post-doc in this role, you will work in collaboration with the Faculty Director (FD) for Computational Marketing, Prof. Harikesh Nair, to create cutting edge research in computational marketing, social science and business at the Stanford University Graduate School of Business (GSB). You will leverage access to Tera-bye scale data, computing and experimentation opportunities to drive this exciting research agenda forward. You will partner with the FD to publish and present this research in leading industry and academic conferences and journals other fora, and to engage with academia and industry.

The post-doc role gives you an opportunity to draw on a broad set of technological, creative, and problem-solving skills in order to conceive, propose, and deliver innovative solutions to several research challenges. Examples include:

  • Collaborate with the FD and the team to suggest, support and shape new data-driven research on advertising, e-commerce, and marketing through novel combinations of data, computing, and social science methods
  • Help to define relevant questions about user behavior, brand safety, user demand, advertising effectiveness, targeting, pricing, promotions, and develop and implement quantitative methods to answer those questions
  • Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale
  • Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution and answer strategic questions using data
  • Understand the ways in which suppliers, brands, third-party stores, content providers, payment systems and consumers interact in ecommerce businesses and ways by which well-functioning marketplaces can be analyzed and designed
  • Help crystallize research into strategic decisions and communicate them to high level audiences

A key industry partner will be JD.com. JD.com is China’s second largest e-commerce company, and the country’s largest retailer, online or offline counting Tencent, WalMart and Google as strategic investors. JD's 2018 revenues of USD 67B makes it China's largest internet company by revenue: see here (https://jdcorporateblog.com/factsheets/) for more facts about the company. See here (https://gsb-faculty.stanford.edu/harikesh-nair/work-at-jd-com/)for examples of recent research.

Preferred Knowledge and Skills

The ideal candidate for this position will be quantitatively trained with expertise in statistical and econometric methodologies, and with a solid understanding of causal inference and social science methods. Individuals with interest in understanding consumer behavior, firm behavior, competition, with a passion for business problems, and an interest in combining data and strategy will thrive in our environment. Our ideal candidates will be able to leverage their training and their skills to leverage data and tecnology. They will be comfortable working cross-functionally and thrive at working in an area at the intersection of academia and industry, and in using math and data to solve practical, high-scale problems. Interest in e-commerce, China and Asia, and in economics/quantitative marketing/business-analytics is a plus.

Minimum Qualifications

  • Fluency in big-data tools for data manipulation and analysis (SQL/Hive/PySpark)
  • Knowledge of R and/or Python (Matlab/Stata)
  • Training and experience in using advanced quantitative methods (statistics, econometrics, ML, RL, causal inference, experimentation)
  • Must understand potential outcomes framework and have familiarity with causal inference methods such as split-testing, instrumental variables, difference-in- difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators. Knowledge of structural econometric methods, a plus.
  • Experience with large datasets and interest in consumer behavior, e-commerce and related business questions
  • PhD degree or combination of education and relevant experience in a quantitative discipline such as quantitative marketing, economics, operations research, statistics or engineering data-science and related disciplines.

PHYSICAL REQUIREMENTS*:

  • Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.
  • Occasionally use a telephone.
  • Rarely writing by hand.

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

WORKING CONDITIONS:

  • Some work may be performed in a laboratory or field setting.

WORK STANDARDS:

  • Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
  • Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
  • Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu

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.

Application procedure

To apply, send a message to zulliger@stanford.edu. (See posting for details.)

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