The Predoctoral Fellowship Program at Columbia Business School provides an opportunity to gain experience in academic research, with a special focus on fields such as Accounting; Decision, Risk & Operations; Economics; Finance; Management; and Marketing. The two-year program helps fellows earn admittance to PhD programs and prepares them for successful graduate-school careers. Predoctoral fellows are hired as staff associates and paid a salary.
Under the guidance of CBS faculty, the predoctoral fellow plays an active role in multiple research projects that are in various stages, from conception through publication. The work may include but is not limited to: webscraping, cleaning and merging data, literature reviews, and statistical analysis. When predoctoral fellows make significant intellectual contributions to a research project, they may be offered authorship.
In addition to being mentored by CBS' leading researchers, predoctoral fellows have the opportunity to take one course per semester in disciplines such as sociology, psychology, business, economics, mathematics, computer science, statistics, and data science. Fellows are expected to attend research seminars and are integrated into the CBS research community. In the second year of the program, a faculty member helps guide the predoctoral fellow through the PhD application process.
Candidates should be intellectually curious, have the ability to learn new skills independently, and have a demonstrated interest in research. A bachelor’s degree is required. Candidates must have demonstrated strong quantitative skills or have an undergraduate or graduate degree in economics or finance, or in a quantitative discipline such as electrical engineering, computer science, operations research, mathematics, or statistics. It is expected that the candidate will apply to a PhD program after completion of the first year, although some students ultimately take jobs in industry. Candidates should have advanced knowledge of and motivation to learn more technical skills pertaining to the use of statistical and mathematical software and programming languages, including Stata, R, Python, and MATLAB. Having a background in advanced mathematics (linear algebra, optimization) and statistics (regression analysis) is helpful. Knowledge of other programming languages (SAS, SQL, C/C++) and software packages (Tableau, ArcGIS, etc.) is considered a plus.
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