Research Assistant (25%) wanted

Are you enthusiastic about management research? Do you enjoy collecting and analysing quantitative data? Are you able to work both independently and in a team in a highly dynamic environment? Are you available for one year at least? 

We are looking for highly motivated students interested in gaining quantitative and research experience at the Chair of Strategic Management and Innovation (SMI) / Prof. Dr. Georg von Krogh at the Department of Management, Technology, and Economics.

We offer a flexible part-time work paid hourly, with up to 25% employment monthly. Varied tasks await you in our team, such as supporting us with data collection (e.g., web scraping), performing quantitative data analyses with structured and unstructured data etc.

You should be a student enrolled in a bachelor's (preferably late stage) or master's program (preferably early stage) in Switzerland.

Excellent English skills (C1-C2) are required. We welcome applicants demonstrating a strong ability to conduct statistical and econometric analyses with empirical data (e.g., company financial data) in R or Python. The level should be similar to the tasks explained in “Introductory Econometrics: A Modern Approach” and “Econometric Analysis of Cross Section and Panel Data” by Jeffrey M. Wooldridge.

Understanding of Basic Statistical Concepts:

• Descriptive statistics (mean, variance, standard deviation)
• Probability distributions
• Hypothesis testing

Regression:

• Simple and multiple regression analysis
• Panel data analysis, fixed effects, and random effects models

Assumptions and Diagnostics:

• Assumptions of the classical linear regression model
• Detection and handling of multicollinearity
• Heteroscedasticity and autocorrelation diagnostics

Model Specification:

• Selection of appropriate functional form
• Inclusion of relevant variables
• Dummy variables and interaction terms

Endogeneity and Instrumental Variables:

• Understanding endogeneity issues
• Instrumental variables estimation

Limited Dependent Variables:

• Binary choice models (Logit, Probit)
• Count data models (Poisson, Negative Binomial)

Software Proficiency:

• Familiarity with statistical software (e.g., R, Python, Stata)
• Ability to code and run econometric models

Data Management:

• Data cleaning and manipulation
• Merging and handling large datasets
  
  

Your application must include a cover letter motivating your interest in the position, your academic transcripts, and your CV. The starting date is flexible.

Please send your full application to Rudolf Maculan - .

Thank you for your interest. We are looking forward to receiving your application!