Academic Employee (f/m/d) Applied Data Science (Campus Hamburg)

TN Germany; Hamburg; Vor Ort; EUR 50.000 - 70.000; Vollzeit

Zusammenfassung

A leading university in Germany seeks an Academic Employee for its Hamburg campus. The role involves teaching applied data science courses, supervising students, and contributing to academic administration. Ideal candidates will have a Master's or Doctorate and experience in data science education.

Leistungen

Company pension scheme

Local transport pass

Professional training at special rates

Qualifikationen

• Completed university studies in a related field.
• Experience in teaching and research in applied data science.

Aufgaben

• Teaching competence-oriented courses at the Master's level in English.
• Supervision of case studies, internships, and master's theses.
• Participation in the development of the study program.

Kenntnisse

Teaching

Machine Learning

Natural Language Processing

Deep Learning

Intercultural Competence

Ausbildung

Master of Science

Doctorate

Tools

Python

R

Tableau

OpenRefine

Jobbeschreibung

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Academic Employee (f/m/d) Applied Data Science (Campus Hamburg), Hamburg

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 across 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. Students learn in a purposeful and independent manner according to the specially developed study concept CORE (Competence Oriented Research and Education). The university emphasizes scientific and practice-based thematic units to prepare students for professional life. SRH Hochschule Heidelberg is state recognized and accredited by the German Science Council.

We are part of SRH, a leading provider of education and healthcare services with 16,000 employees. SRH operates private colleges, education centers, schools, and hospitals.

We are seeking an Academic Employee (f/m/d) with a focus on teaching for our new Hamburg campus, to start as soon as possible on a full-time basis. The position is initially limited to two years.

What you can look forward to:

• Professional training at SRH at special rates.
• A contract that includes a company pension scheme, the possibility of part-time work, a performance-related salary, and a local transport pass.
• Teaching competence-oriented courses at the Master's level in English, focusing on handling large data sets, machine learning, natural language processing, and deep learning.
• Supervision of case studies, internships, and master's theses.
• Providing intensive support to students in their case studies each semester.
• Handling and maintaining data infrastructure.
• Participation in the development of the study program and in academic self-administration.
• Supervision of research projects and master's theses in relevant areas.

Your profile:

• Completed university studies (Master of Science or doctorate preferred) in a related field.
• Experience in teaching and research in applied data science, with openness to new technologies.
• Practical knowledge of programming languages such as Python and R.
• Experience with tools like Tableau and OpenRefine.
• Relevant publications in these areas.
• Independent and responsible working style.
• Excellent English skills, high intercultural and social competence.

We value diversity and welcome applicants regardless of gender, nationality, ethnic or social background, religion/belief, disability, sexual orientation, or gender identity. Disabled applicants with equal qualifications are given preference.

For further information, please contact Felicitas Völkel at the provided phone number.

Join our team and apply online including the code. SRH Hochschulen GmbH, HR department | Hanife Öztürk.

We would like to point out that application documents will be shared with the appointment committee and may be accessed by external parties such as practice experts, other universities, or student representatives, in accordance with appointment regulations and state laws.