Research Associate for the Project “BERD@NFDI” PostDoc in Applied Data Science in Marketing § 28 Subsection 3 HmbHG

Our research team at the Chair of Marketing and Customer Insight (Prof. Dr. Mark Heitmann) applies quantitative data analytics to solve relevant managerial problems. We are experts in leveraging machine learning and artificial intelligence to revolutionize marketing. In particular, we rely on social media analytics, need and trend research, or deep learning-based product design. Our results help to improve social media marketing, product, brand and customer management, and direct-to-consumer business models.

We are members of the consortium BERD@NFDI. The federal and state governments fund BERD@NFDI to build a cutting-edge data infrastructure for business, economic and related data. Our task is to provide an overview of machine learning applications in business administration and related fields.

Responsibilities

Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications.

Specific Duties

We are looking for strong candidates interested in facilitating big data analytics and machine learning. Your tasks will include:

• team lead, no previous experience required
• coordinating and collaborating within the BERD@NFDI consortium, with leading national and international research institutions
• representing the group at national and international conferences
• conducting high-quality research projects to be published in international top journals
• collaborating and supporting knowledge transfer, incl. academic spin-offs

Requirements

An above average university and doctorate degree in a relevant field (Business Administration, Economics, Data Science, Mathematics, Engineering).

Excellent research skills, computational and analytical abilities, proficiency in English and German, and superior communication skills are required. A strong scientific track record, including first publications in peer-reviewed journals in the field of customer insight and consumer behavior. Proficiency in analyzing and interpreting survey/experimental data (preferably in Python).

We offer

• strong research track in business administration, ranked among the top 3 in Germany by the business press over the last years
• a modern work environment with flat hierarchies and an open, collaborative culture
• work on your research articles in small teams, where everyone is fully involved
• possibility of your own office in our art nouveau building in the center of Hamburg
• office equipment incl. cutting-edge IT (dedicated AI infrastructure for our team, access to Windows & Linux servers with multiple GPUs)
• flexible home office solutions
• potential to build your personal reputation by playing a key role in the national research data infrastructure for business administration and related fields
• one of the largest and most successful marketing areas in Germany, with 5 full professors and 30 PhDs and postdocs, many with consulting background and industry expertise
• encouragement to transfer your research into entrepreneurial action, learn from our experience in awardwinning spin-offs/startups
• leverage an excellent national and international academic network and participate in international conferences

As a University of Excellence, Universität Hamburg is one of the strongest research universities in Germany. As a flagship university in the greater Hamburg region, it nurtures innovative, cooperative contacts to partners within and outside academia. It also provides and promotes sustainable education, knowledge, and knowledge exchange locally, nationally, and internationally.

The Free and Hanseatic City of Hamburg promotes equal opportunity. As women are currently underrepresented in this job category at Universität Hamburg according to the evaluation conducted under the Hamburg act on gender equality (Hamburgisches Gleichstellungsgesetz, HambGleiG), we encourage women to apply for this position. Equally qualified and suitable female applicants will receive preference.

Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.