First of all, thank you for your interest in working with me! Please see some of the details below about my specific criteria and constraints in hiring RAs/TAs and admitting PhD students, and please only contact me after ensuring that you satisfy these criteria.
I am willing to work with RAs at both the undergraduate and graduate level, but note that I am only able to hire current UBC students as RAs, so I am unable to hire alumni and non-UBC students. Additionally, I do not consider unpaid RA-ships; I will only work with you if you are being compensated either financially or with academic credit (e.g., through directed studies; see below).
I mostly hire RAs on a part-time basis to work on tasks as they come up, but I might consider more intensive engagements (e.g., full-time summer work through the WLIURA program) under the right conditions, namely a sufficiently strong fit in terms of skills and interests.
Here are some of the skills and qualities I look for in RAs, in decreasing order of importance. They are not all strictly necessary, but the more boxes you're able to check, the more likely that I will be able to find appropriate work for you to do.
Initiative and attention to detail: Ability to take initiative to find and troubleshoot problems, and to check solutions carefully.
Academic interest: A strong interest in reading and conducting academic research, especially quantitative research.
Programming skills: High comfort with R and/or Python, especially libraries for data processing like pandas and data.table.
Statistics/econometrics: Familiarity with statistical models and methods like multiple regression, fixed effects models, maximum likelihood estimation, and simulation.
Linear algebra: A good grasp on basic linear algebra concepts like matrix multiplication, inversion, and eigendecomposition.
Machine learning: Familiarity with machine learning models and libraries, including libraries for off-the-shelf methods like in scikit-learn, but also custom/modular libraries like PyTorch.
Bayesian/probabilistic methods: Exposure to Bayesian inference and computational methods like MCMC and variational inference, and willingness to learn probabilistic programming languages like Stan, Pyro, and NumPyro.
If you are a current UBC student who satisfies most of the above criteria, feel free to reach out to me at shin.oblander@sauder.ubc.ca. Please indicate your skills and interests based on the above list, include a transcript/resume, and indicate your general timeline and availability (i.e., what sorts of hours you're looking to work, and over what time horizon).
TA applications at UBC Sauder are handled through a centralized system; please submit an application through there rather than contacting me directly. Currently, the only course I teach that has TA funding is COMM 363: Marketing Analysis. I can only hire current UBC students, and I strongly prioritize TAs who have taken the course from me in the past.
I may consider supervising undergraduate/masters student projects through academic credit-bearing programs like directed studies or honours theses; however, I have very limited capacity to supervise such projects and will only consider clear, well-scoped project ideas that align with my areas of research expertise and interest. Please consult the desired skills in the RA section above and my research agenda outlined in the "My Research Agenda" section below to get a sense of what I am looking for. If you are a current UBC student and feel you are a strong fit for these criteria, please reach out to me at shin.oblander@sauder.ubc.ca with a brief description of your project idea and include your transcript and resume.
UBC Sauder's PhD program applications are handled through a centralized system. Please consult this page for more details on the requirements and expectations for applying, and this page for more details on the Marketing PhD program specifically (I generally work with students in the "Modelling" or Quantitative track of the Marketing PhD program).
Unlike other fields, the quantitative marketing group at Sauder does not follow a lab-based system where PhD students are admitted to work with a specific professor. Instead, the faculty in my group collectively decide on PhD admissions, and students work on projects with multiple faculty in their first couple years before choosing an advisor. As such, please do not contact me directly if you are interested in the PhD program, but instead submit an application through the Sauder system.
If you are interested in working with me specifically, please indicate this in your application (e.g., in your Statement of Intent). Please also consult the "My Research Agenda" section below to check whether we are a good fit.
Our PhD program is fully funded for the first 5 years, and because the program is funded through our research grants, we have very limited capacity to admit PhD students, and admissions are very competitive. While applications are very competitive and I cannot guarantee outcomes, here is a rough sense of the typical characteristics of an admitted quantitative marketing PhD student. They are not all strictly necessary, but our admitted students usually have most of these qualifications.
Academic background: An undergraduate and/or masters degree in a related discipline such as Economics, Statistics, Mathematics, Computer Science, or Business (in a quantitatively-oriented specialization) with around an "A" or "A-" average.
Specific coursework: Good grades in graduate-level courses in foundational topics such as microeconomic theory, probability theory, linear algebra, statistics/econometrics, machine learning, and real analysis.
Test scores: Approximately 95th percentile scores in the GMAT or GRE, and near-perfect scores in IELTS or TOEFL if your previous degree program did not have English as its primary language of instruction.
Research ability: Demonstrated potential to independently conduct rigorous academic research, from idea generation to execution.
Discipline fit: Demonstrated interest in marketing-related research topics with a quantitative focus.
If you are already a PhD student at UBC in a non-marketing program and are interested in working with me on a specific project, or having me on your examination committee for your dissertation, please consult the "My Research Agenda" section below and feel free to contact me at shin.oblander@sauder.ubc.ca if you think your project/dissertation fits within this agenda.
I do not have funding to hire a postdoctoral fellow at this time. I may consider working with a postdoc under exceptional circumstances (e.g., if we have very complementary skills and research interests; see the "My Research Agenda" section below), but this would necessitate a successful application for external funding such as a fellowship from SSHRC.
Generally, I am most likely to consider working with students with strong technical skills whose research interests overlap with mine. Here is a non-exhaustive list of my current research interests and priorities, so you can get a sense of if we are a good fit.
Methods:
Probabilistic machine learning: Applying models and computational methods from the probabilistic machine learning literature (especially Bayesian inference of latent variable models) towards addressing novel marketing problems, including all of the areas outlined below (see Dew et al., 2024).
Representation learning: Developing machine learning models to extract useful information from complex, high-dimensional/unstructured data. In particular, I am interested in designing models to explicitly incorporate inductive biases and geometric structure in their representations that are desirable for downstream tasks (see Oblander, 2025 and Dew et al., 2024, Section 5.1).
Causal inference: Developing and evaluating statistical/econometric methods to infer the causal effects of "treatments" or events on consumer behaviour, particularly drawing on insights from customer base dynamics, predictive modelling, and Bayesian nonparametric methods (see Oblander and McCarthy, 2023 and Dew et. al, 2024, Section 5.2)
Data fusion: Developing statistical methods to analyze problems involving multiple sources of data that may have differing units of aggregation, degrees of missingness, and selection biases (see McCarthy and Oblander, 2021 and Dew et. al, 2024, Section 3.4).
Substantive areas:
Customer relationship dynamics and subscription programs: Understanding the dynamics of customer relationships (e.g., adoption, retention, and repeat purchase decisions), and especially the implications of the rise of subscription-based business models on consumers, firms, and markets (see McCarthy et al., 2025 and Oblander and McCarthy, 2023).
Bounded rationality models of consumer behaviour: Empirically modelling and testing behavioural theories of how consumers deviate from rationality and assessing their implications on businesses and public policy. I am especially interested in linking representation learning models with cognitive models to try to understand how people represent information internally (see Oblander, 2025 and Dew et al., 2024, Section 5.3).
Intersection between marketing and urban planning: Linking the methodological and substantive perspectives of marketing with those of urban planning and transportation research to understand how transportation patterns shape consumer behaviour, and vice-versa. I am particularly interested in understanding the implications of land use patterns and transportation infrastructure (e.g., proximity to rapid transit, pedestrianization of a retail corridor) on people's movement patterns and shopping behaviour. This is not something I work on yet, but am interested in working on in the future.