Research and Teaching Associate
- Vienna University of Economics and Business
- Research, development, teaching
- Part time
- published till: 14.10.2026
Institut für Retailing & Data Science
Part-time, 30 hours/week
Starting as soon as possible and ending after 6 years
The Institute for Retailing & Data Science (RDS) works at the intersection of quantitative marketing, machine learning, and causal inference. We study how digital markets work - from grocery retailing to music streaming. Through long-standing partnerships, we have access to large-scale, real-world datasets from major retailers and online platforms, including transaction-level retail data, streaming-data from the music industry, and data we collect ourselves at scale via APIs and automated data collection.
Our research combines modern data science methods - machine learning, deep learning, and causal ML - with state-of-the-art econometric approaches, such as quasi-experimental designs (e.g., modern difference-in-differences estimators), to answer questions that matter to managers, platforms, and policymakers. We publish in leading international journals, present at top conferences in marketing, economics, and machine learning, and actively share our findings with the broader public. In teaching, we prepare the next generation of marketing professionals to shape the digital transformation of the retail sector.
You will join a vibrant, internationally oriented team with a strong collaborative culture, close ties across WU, and an active international research network.
What to expect
- Writing a dissertation (one third of your working hours): You will develop your own research agenda within one of our active research streams, working with real market data from day one. Current research streams include:
- AI-generated content in markets for information goods: Generative AI is rapidly transforming markets such as music streaming. Dissertation topics include detecting AI-generated content and streaming fraud, estimating the impact of AI content on demand, revenues, and creators, and evaluating policy interventions such as AI-content labels.
- Retail and platform analytics: Demand modeling, shopping basket analysis, pricing and promotion analytics, personalized recommendations, and channel management; using transaction data from major retail partners.
- Causal inference and machine learning: Estimating causal effects in large-scale market data, using approaches ranging from modern difference-in-differences designs to double/debiased machine learning and heterogeneous treatment effect estimation - with applications to demand estimation, media exposure, pricing, and promotions.
- Bilateral AI - combining sub-symbolic and symbolic methods: Integrating deep learning with symbolic reasoning and causal knowledge (causal representation learning) to build models that are both predictive and interpretable.
- Data & infrastructure: You will work with large, proprietary, real-world datasets (e.g., from major retailers and online platforms) and modern tooling for large-scale data processing.
- Conferences & network: You will present your work at leading international conferences and collaborate within our international academic network.
- Teaching & co-supervision: The regular teaching load is approx. one course per term; you will also co-supervise bachelor's and master's theses.
- Organizational tasks: As part of the team, you will support selected organizational tasks of the institute.
What you have to offer
We are looking for a candidate who meets the following criteria:
- You hold a diploma or master's degree with strong academic performance in business, economics, computer science, data science, statistics, or a related quantitative field that qualifies you for enrollment in a doctoral program at WU.
- You have a strong quantitative mindset and genuine enthusiasm for empirical research - you enjoy working with large-scale, real-world data and turning it into credible evidence.
- You are proficient in at least one statistical programming language (e.g., Python, or R) and eager to deepen your technical skills as part of your doctoral training.
- You are interested in the methods we use across the full research pipeline - automated data collection (e.g., web scraping, APIs), large-scale data processing (e.g., SQL databases), machine learning and deep learning (e.g., gradient boosting, PyTorch), and causal inference (e.g., quasi-experimental designs, double/debiased ML).
- You bring experience in or curiosity about our substantive domains (retailing and digital platforms), such as demand modeling, pricing and promotion analytics, recommender systems, channel management, or the economics of AI-generated content.
- You have an excellent command of written and spoken English; German language skills are an asset.
- Since the position includes teaching responsibilities, prior teaching or instructional-support experience (e.g., tutoring, grading, course assistance) is appreciated.
- You are prepared to use multimedia teaching and learning formats.
What we offer you
- Six weeks of vacation/Extra vacation time
- Flexible working hours
- Training opportunities
- A wide range of benefits, from an in-house medical officer to athletic activities and a meal allowance to a variety of employee discounts
- Meaningful work in an open-minded, inclusive, and family-friendly environment
- A modern campus with spectacular architecture in the heart of Vienna
- Inspiring campus life with over 2,400 employees in research, teaching, and administration and approximately 21,500 students
- Excellent accessibility by public transportation
The minimum monthly gross salary amounts to €2,832.08 (14 times per year). This salary may be adjusted based on job-related prior work experience. In addition, we offer a wide range of attractive social benefits.
Do you want to join the WU team?
Then please submit your application by October 14, 2026 (ID 2910).
We are looking forward to hearing from you!
WU is committed to diversity and inclusion and actively encourages applications from people of all backgrounds. We particularly welcome applications from women. Not sure if your profile fully matches our requirements? Apply anyway! In case of equal qualification, female candidates will be given preference. Applicants with disabilities will be supported throughout each stage of the recruitment process. Candidates' qualifications will be assessed in the context of their academic age.
