Professor Applied Data Science and Analytics (Campus Hamburg) f / m / d

Professor Applied Data Science and Analytics (Campus Hamburg) f / m / d

SRH Hochschule Heidelberg is one of the oldest and largest private universities in Germany. It is state recognized and accredited by the German council of Science and humanities. Currently, 3,000 students are enrolled at six Schools. The university offers future-oriented courses in economics, computer science, media and design, engineering, social, legal and therapeutic sciences as well as psychology. At the SRH universities, students learn in a purposeful and independent manner according to the specially developed study concept CORE (Competence Oriented Research and Education). In compact scientifically and practice-based thematic units, the students are qualified for professional life by activating teaching as well as competence-oriented exams.

We are looking for a Professor for Applied Data and Analytics f / m / d for our new campus in Hamburg in full or part-time.

What you can look forward to:

• A diverse, exciting position with self-responsibility at a growing, international and future-oriented university.
• Professional training is a priority at SRH – you will benefit from our complete range at special rates!
• The contract includes a company pension scheme, the possibility of part-time work, a performance-related salary, and a local transport pass.
• Competence-oriented teaching at the Master\’s level in English, in the field of handling large amounts of data and machine learning, natural language processing, and deep learning.
• Supervision of case studies, internships, and master thesis projects.
• Research and publications in the field of Data Science.
• Acquisition and implementation of – preferably interdisciplinary – research projects and projects with partners in professional practice.
• Participation in the further development of the study program and in academic self-administration.
• Participation in the university\’s international activities, particularly in the expansion and intensification of our network.
• Willingness to travel to or work at other locations is an advantage.

Your profile:

• Fulfillment of the employment requirements for professors according to § 47 LHG BW.
• Special aptitude for scientific work is usually demonstrated by a qualified doctorate or excellent subject-related practice achievements.
• Knowledge of Designing and Implementing Statistical Models, Statistical Data Analysis, and Machine Learning.
• Intelligent merging, processing, and analysis of complex and large datasets.
• Practical knowledge with teaching experience in programming languages such as Python, R.
• Experience with tools such as Tableau, Open Refine, Trifacta.
• Relevant publications in the domain of Applied Machine Learning and Data Engineering.
• Experience with handling and maintaining data infrastructure.
• Independent and responsible way of working.
• Substantial teaching experience at the university level is recommended.
• Excellent written and spoken English knowledge.
• Willingness to actively develop and accompany learning processes according to the CORE Principle.
• Willingness to use innovative teaching concepts and their further development using digital media and technologies as well as enjoyment of educating our students.
• A network of contacts in the data science industry is advantageous.

We welcome diversity and emphasize that we welcome all people equally, regardless of gender, nationality, ethnic and social background, religion/belief, disability, sexual orientation, and identity. Disabled applicants with equal professional and personal qualifications are given preference.

For further information, please feel free to contact Felicitas Völkel.

Join our team and apply online including the code.

We would like to point out that the application documents are made available to the appointment committee and thus also to external third parties who are not part of the university. Depending on the federal state, these are experts from practice or other universities or student representatives who are members of the appointment committee according to the appointment regulations in application of the respective state university laws.

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