Call for Applications: Mathematics for Data Science in Fall 2026

The Hertie School, The University of Governance in Berlin, is hiring an instructor to teach the core course Mathematics for Data Science for first-year Master of Data Science (MDS) students, in the Fall 2026 semester (31 August to 4 December 2026), on a contract for works and services basis.

This course should deliver a compact and tailored introduction to the core mathematical concepts of data science. Linear algebra, probability and statistics, and optimization are mathematical pillars underlying the practice of data science. The course covers the core concepts across these three pillars and accompanying modern computational tools of mathematics. Upon completing the course, students should have a broad understanding of linear algebra, probability and statistics, and optimization necessary for data science.

The course is part of the “Foundations of Data Science” module for our MDS program, which builds students’ foundational understanding and intuition in statistics and programming by introducing core concepts and best practices of computer programming and the mathematics essential for data science. Through this module, students will develop a broad and critical grasp of the mathematics underpinning modern machine learning and deep learning algorithms, knowledge that is key for subsequent modules. Students will also be able to apply programming practices to solve real-world coding challenges.

This is an established course in the MDS program, and a complete set of teaching materials — including a syllabus, lecture slides, problem sets, and lab activities — is available for the incoming instructor to use as is or adapt to their own style. There is no need to design the course from scratch.

Instructor duties

Review and, if desired, adapt the existing course syllabus, and confirm the final version by June 15, 2026.
Deliver 12 weekly sessions of 100 minutes each in the Fall 2026 semester (31 August to 4 December 2026) in English, drawing on the existing lecture materials. The standard format is onsite, but online options can be discussed.
Distribute and grade assignments and exams (if applicable); existing problem sets and assessment materials are available and can be reused or modified.
Set course readings and make them available to students, with support from the Hertie School Library. A reading list from previous iterations of the course is available as a starting point.

Instructor profile

Solid background in linear algebra, probability, statistics and optimization (at advanced undergraduate, Master- or PhD-level)
Excellent pedagogical skills.
Preferred: teaching experience at the graduate level.

What we offer:

Competitive teaching honorarium.
Travel expenses lump-sum (for instructors based outside Berlin).
Administrative support via the Adjunct Support Team.
For digital teaching: support by the Digital Learning Designer of the Hertie School

For further information, please contact Dr. Gabriel Tarriba, Director of Curricular Affairs at the Hertie School, at tarriba@hertie-school.org. If you are interested in this teaching assignment, please submit a current CV, and, if available, any teaching evaluations to this email address. Interviews will be conducted on a rolling basis. Priority deadline for applications: May 15, 2026.