Mentor Areas
- Quantitative Marketing
- Applied Data Science and Analytics
- Statistics, especially Bayesian Statistics
- Machine Learning
- Computational Social Science
Description:
I am seeking research assistants to help develop the next generation of tools for marketing analytics, including:
- Methods for learning consumer preferences from unstructured image, text, and video data
- New solutions for modeling customer lifetime value and predicting customer behavior
- Advanced machine learning tools for optimizing marketing.
This is a technical position that requires extensive prior coding experience.
Research assistants will be asked to help across my portfolio of ongoing quantitative marketing research projects. These projects involve working with a variety of data sources, including company-supplied, web-scraped, and experimental data, to push the boundaries of what marketers can do with data. As a research assistant, your job will involve helping on day-to-day data processing, coding, and development work, including:
- Data cleaning and processing
- Exploratory data analyses, including regression and visualization
- Scraping data from the web
- Designing complex surveys and web applications to deploy new methods in practice
- Programming new models in languages like Python, PyTorch, Stan, Pyro, and JAX
- Conducting reviews of the academic literature
- Creating user-friendly code repositories to help bring these models to practice
Please note that this is an academic research job, NOT an applied marketing job.
Preferred Qualifications
Minimum requirements:
- Advanced proficiency with base Python
- Experience with applied data science libraries like Numpy, Pandas, and Scikit-learn
- Basic proficiency with Git
I prefer hiring candidates who also have knowledge of some of the following tools:
- Probabilistic programming languages like Stan, PyMC, Pyro, NumPyro
- Deep learning libraries like PyTorch and TensorFlow
- High-performance computing tools like JAX
- Web development tools like Javascript and React
- R
- Experience with cloud computing environments or high performance computing clusters
Project Website
Learn more about the researcher and/or the project here. http://www.rtdew.com/
Details:
Preferred Student Year
Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.