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Master in Applied Data Science
120ECTS
*excl. 350 EUR enrolment fee and 100 EUR application fee
Our Master in Applied Data Science programme is designed for graduates with strong mathematical skills, preparing them to excel in the dynamic fields of applied AI and data science. The programme builds on quantitative foundations, offering a comprehensive understanding of data science to address real-world business challenges.
With a unique curriculum blending applied machine learning, data science, business, and finance, students gain a well-rounded education suited to today's business needs. The 3-Day Model allows for part-time work alongside studies, offering practical experience.
Students work on industry projects with top companies and startups, gaining hands-on experience. They receive mentoring from renowned faculty and access to an Entrepreneurship Accelerator or Incubator to develop their own ventures. The programme also covers the ethical and legal aspects of AI, equipping students to navigate the fourth industrial revolution.
Rankings & Accreditations
Frankfurt School is one of the best European Business Schools. Accredited by AACSB, EQUIS and AMBA, the three leading international associations of business schools. Frankfurt School is one of the few institutions worldwide, which has been awarded the so-called "Triple Crown".
The Master in Applied Data Science's Curriculum
Our Master in Applied Data Science programme has four pillars expertly designed for a cohesive and expansive educational journey.
Technological Mastery: The first pillar immerses students in the theoretical and technological foundations of data science and artificial intelligence to cover essential domains such as algorithms and data structures, computational statistics, machine learning, deep learning, and cloud computing, among others, laying the groundwork for technical proficiency.
Business Process Integration: The second pillar exposes students to the symbiotic relationship between data science and business processes, highlighting how data-driven insights drive better business operations and decision-making.
Ethical and Legal Awareness: The third pillar provides a critical examination of the ethical and legal landscapes in data science and artificial intelligence, preparing students to navigate the moral complexities and risks posed by statistical technology.
Practical Business Applications: The final pillar focuses on the practical application of data science and artificial intelligence within business, enabling students to translate their knowledge into actionable solutions that drive business innovation and growth.
★ Free pre-courses in Python and Mathematics are offered in August prior to the study programme.
Quantitative Fundamentals
Students will acquire a rudimentary understanding of linear algebra, probability theory, information theory and their use in machine learning and data science. Paying particular attention to mathematics for information systems, this module serves as a foundation module for Machine Learning 1 & 2.
Lecturer
Algorithms & Data Structures
Using Python, this module provides you with an introduction to basic algorithms, as well as the design analysis of algorithms and data matching structures. This allows you to implement taught algorithms and learn the basics of Python.
Lecturer
Intro to Data Analytics in Business
Data Analytics plays an important role in both industrial projects and academic research. It encompasses methodologies, algorithms, and processes aimed at providing insights into high-dimensional datasets. While classical statistical approaches may not be applicable in this context, novel data-analysis techniques have emerged within the realm of machine learning. These methods are now widely used in both scientific research and practical applications, leveraging the computational power offered by modern computer technologies.
This module serves as an introduction to Data Analytics, focusing on computational techniques and algorithms designed to discover and analyze patterns, even within large-scale datasets. Covered topics include data analysis, visualization, segmentation, classification, prediction, and decision-making. You will have the opportunity to implement and apply these methods using the Python programming language and related libraries.
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Lecturer
Computational Statistics & Probability
This course introduces causal inference and generalised linear multilevel models from a Bayesian perspective. The aim of the course is to give you a hands-on introduction to the fundamentals of statistical modeling. We will cover the basics of regression up to advanced multilevel models, focusing on the algorithmic details throughout the course to build your understanding of and confidence with model-based computational statistics.
Lecturer
The Language of Business
This module serves as introduction to accounting as the language of business and its various purposes and applications. On a very fundamental level, accounting statements are a primary source of systematic public information about businesses, providing the basis for answering many relevant questions. As such, it is important for those interested in business data analytics.
Lecturer
Databases and Cloud Computing
Nowadays everyone is aware of the ever-growing importance of the data streams fueling the economy and becoming the future catalyst for our society. Learn how to master these streams by understanding the key concepts of the most important frameworks and technologies for data storage and management.
External Lecturers
Prof. Dr. Peter Roßbach & Dr. Jörg Gottschlich
Machine Learning 1
This module is a hands-on, case-study based introduction to contemporary regression-based techniques in machine learning. Machine Learning 1 has a focus on supervised learning algorithms (used to make accurate predictions about the future from current data) and unsupervised learning (used to discover unknown structure in your current data).
Lecturer
Guided Studies in Financial Management
The course provides an introduction to financial management, including capital budgeting and capital markets. The main focus is on designing and conducting empirical analyses in small teams.
External Lecturer
Dr. Stefan Scharnowski
Machine Learning 2
This hands-on module focuses on statistical machine learning and probabilistic data analysis involving highly parameterised models. Topics include time series analysis, variational inference, graphical models and unsupervised learning. You will learn how to implement supervised and unsupervised machine learning models and gain an understanding of the computational challenges faced when performing statistical inference on high-dimensional data.
Lecturer
AI & Humanity: The Ethics of Data Science
This module explores the ethical and legal questions that information technologies raise for issues such as privacy, responsibility or fairness. Participants will gain an in-depth comprehension of legal and ethical issues surrounding information technologies, as well as the crucial legal and ethical questions that we should ask about such technologies. On successful completion of this module, students will have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to ethical and legal problems and develop solutions to those problems.
Lecturer
Strategy and Performance Management
This module gives you the latest insights into strategy development and execution with a strong emphasis on organisational and machine learning on data analytics. Students become acquainted with models, tools and techniques to develop, analyse and execute organisational strategy and its success.
Lecturer
Deep Learning
This module covers deep neural networks, which are currently the “workhorse” of machine learning and most commonly used methods. Our main purpose will be to understand the theoretical background necessary to employ deep neural networks to solve problems of image recognition and language processing. Particularly, we focus on different theoretical concepts to make deep neural networks which are thus essential for building successful applications. The module has a practical focus, taking theory and then applying it immediately in each class.
Lecturer
Natural Language Processing
This module is focused on applying machine learning techniques to gain language understanding. Natural language processing is one of the main sub-fields of machine learning and has driven major algorithmic break-throughs in recent years. Language is a form of time series so break- throughs in natural language processing such as LSTM networks have been closely connected to advances in machine learning in general.
Lecturer
Cooperation Company Project
This module is a practical project conducted with a partner company which allows students to apply the skills they have learned during other semesters. Students will work in groups of 3-4 on small, current data science projects within the company under the supervision of a professor and company representative. Students will learn how to illustrate and decompose business problems as well as cleaning and managing data at all stages and then applying data science and machine learning to create a service or software for the project.
Lecturer
Electives or Entrepreneurship or Study Abroad
A range of electives allows you to tailor your Master in Applied Data Science through a diverse and distinctive structure of time formats. Electives are taught not only by in-house faculty but also by leading international practitioners, providing you with the tools to meet your personal aspirations. Elective options are published during the third semester and students must choose elective modules to start in their last semester.
Students have the opportunity to replace their elective modules with either a semester abroad at one of our international partner universities or take part in our Entrepreneurship module.
Master Thesis
You are required to conduct independent research in order to complete your Master's thesis. You will review relevant scientific publications and acquire an in-depth knowledge in the respective field before applying research methods and writing concepts to structure your work. The thesis period is typically three months and takes place during the 4th semester.
Learning Goals
Knowledge and understanding
Graduates will have in-depth knowledge and a critical understanding of the key theories, principles and methods in Data Science. They will be able to identify, analyse and evaluate complex data problems.
This competency is particularly relevant to and developed in the core modules, such as Algorithms and Data Structures, Introduction to Data Analytics in Business or Computational Statistics and Probability and via various means of teaching, learning and assessment (e.g. projects, programming assignments or exam).
Usage and development of knowledge
Graduates will have the ability to construct and critically assess computational, data-driven models to solve complex data problems in business.
The application of analytical techniques is at the core of almost all modules in the programme. The Master in Applied Data Science encourages not only to learn, but rather to apply models to the classroom (e.g. Machine Learning I and II or Deep Learning).
Communication and cooperation
Graduates will be able to communicate effectively in academic and/or private business contexts. They will formulate technical problem solutions and represent them in discourse. They are responsible team members who address and reflect different perspectives.
These competencies are practiced in many modules such as Introduction to Data Analytics in Business or Guided Studies in Financial Management in which students have to do several week-long projects in order to understand and apply the knowledge and skills they have gained in the module. This competency is furthermore at the core of the Cooperation Company Project. Our students are able to test the knowledge they have learned in previous semesters by working on real business use cases together with leading companies in the Cooperation Company Project. Over a period of approx. two months, students will work closely and cooperatively with the company from the start to finish of the project, thus gaining end-to-end, hands-on professional and personal experience.
Scientific self-image and professionalism
Graduates are practiced collaborators in business environments. They have a thorough understanding of their ethical and legal responsibilities as applied data scientists. They will base their professional activities on theoretical and methodological knowledge.
The development of these competencies is distributed throughout the curriculum and in consequence, follows the natural student journey as they grow academically and professionally. The culmination of students’ individual awareness of their role in Business and Society can be found in their final project, the thesis, and in the core module AI & Humanity – The Ethics of Data Science. On successful completion of this module, students will have a thorough comprehension of central legal and ethical issues surrounding information technologies, as well as the crucial legal and ethical questions we must ask about such technologies. Students will furthermore be able to identify and evaluate legal and ethical problems related to information technologies, develop and critically assess appropriate responses to such problems, and assess their own evaluative outlook critically. Finally, students will have developed and strengthened their analytic and critical skills, as well as their ability to apply those skills to solve ethical and legal problems.
Cooperation Company Project
During the third semester, students test the knowledge from previous semesters by working on real business use-cases together with leading companies. Over 3-4 months, students work closely on their project with the company from start to finish, gaining end-to-end, hands-on experience to prepare them for job market.
A team of PwC data scientists and engineers, inspired by motorsports data analyses, conceived the idea of constructing a slot car racing track with sensors to demonstrate data analytics potentials at exhibitions. Their project focused on creating image recognition for identifying race cars in video streams, innovating car control and monitoring methods, modularizing the backend, and establishing a central communication service for seamless information exchange among frontend, backends, and the cloud.
Students worked on a market trends project to understand market sentiment and investor activity across Xetra and Eurex. Project topics included clustering data, predictive power and analysing relationships between datasets. They used data science and machine learning to assess predictability and ultimately visualise the data with which a report was produced.
Reliable forecasts and robust monitoring are vital for energy distributors to manage peak demand effectively, ensuring grid stability and meeting customer needs, with a focus on detecting external factors influencing demand anomalies. Students developed a Machine Learning Pipeline to detect outliers and forecast utility consumption, showcased through a data-driven dashboard with essential KPIs for easy maintenance. Steps included data pre-processing, shock detection with peak categorization and explanations, model training and testing, and automated demand forecasting with dashboard integration.
In collaboration with NASA Ames Research Center and Frankfurt School, our project aimed to replicate and extend previous findings on using Guided Transfer Learning (GTL) to enhance AI performance on high-dimensional, low-sample-size (HDLSS) RNA-seq data. Given the challenges of overfitting in omics research, our approach builds on NASA’s work by pre-training AI models on large RNA-seq datasets (e.g., recount3) to capture gene expression patterns, enabling more efficient learning on small datasets. By refining GTL techniques, we seek to further reduce human intervention while improving few-shot learning, ultimately advancing AI applications in biomedical research.
The project goal was to develop a computer vision model that can detect the build-up of ice on the guide vanes of gas turbines to increase their efficiency. The key tasks of the students were Image Processing (e.g. Edge Detection, Feature Extraction), Data Augmentation, Modeling (e.g. 3D ResNet Model, Anomaly Detection) and an experiment on InfraRed Camera for capturing ice.
The project aimed to identify fraudsters attempting to bypass EU sanctions by constructing a knowledge graph using Open Sanctions and Open Ownership data. This graph was further enriched with data external services like Grok and geo information for better location-based analysis. To enhance data quality and uncover hidden connections, advanced Graph Data Science techniques were applied, including community detection, entity recognition, and entity resolution. These methods improved the accuracy of identifying sanction circumvention attempts, enabling better enforcement and risk assessment.
In this project, we set out to revolutionize how project platforms inform decision-making for market positioning, training strategies, and project acquisition. Our team developed a scalable solution to extract, process, and analyze job/project postings, leveraging NLP techniques like tokenization, stemming, and keyword extraction. The result? Categorization of projects into focus groups (e.g., ML Engineering, Data Engineering) and a clear view of market trends over time.
Students addressed the pressing issue of human impact on global vegetation and its consequences, aiming to mitigate intervention and investigate the tourism industry's role. They developed skills in data modeling, analysis of satellite imagery, time-series data modeling, geographic data visualization, and effective communication.
Sentyre collects data from trucks driving and developing a smart system to predict the best tyre for a certain truck and position on the truck. Students used data science algorithms and data visualisation to come up with a prediction model on the best tyres for certain trucks. Students also used Jupyter Notebook to show their results.
Navigating financial markets is prone to an overload of information, resulting in the challenge to make the right decision at the right time. This applies to the asset class of digital assets even more. Therefore, our students enhanced the decision-making process by developing 1. a forecasting algorithm for predicting the returns of single digital assets using random forest algorithms and neural networks and 2. by constructing a sentiment indicator for major cryptocurrencies by analysing tweets with the help of NLP algorithms.
Meet us Online and On-Campus
Find out more about our Master programmes by attending one of our Master Information Evenings. Find the program that fits your career goals best.
You can also experience our campus in person and engage directly with representatives from our master's programs during our Open Campus Nights.
Personalise your Master in Applied Data Science
3 Day Model
The Master in Applied Data Science follows a unique time model that permits you to work part-time whilst pursuing your full-time Master’s degree. We call this the "3-Day Model". Students typically attend classes three days a week, on Thursdays, Fridays and Saturdays. This leaves three working days for self-study, language courses or part-time employment.
Entrepreneurship Centre
The mission of the Entrepreneurship Centre is to inspire, connect and provide training to Frankfurt School students as well as to external stakeholders such as investors, alumni, founders and partners. You can choose to take part in our Incubator for year-round guidance or boost your project by taking part in the Accelerator. You can also choose the Entrepreneurship module in semester 4 and make this specialisation part of your degree!
Electives
Students can choose in semester 4 from a wide range electives focused on finance, management, and data science related topics, giving them the opportunity to expand the depth of their Master programme and gain insights in addition to the primary topics in other areas of interest depending on their professional goals.
Study Abroad
The Frankfurt School has established partnerships with over 90 universities around the world, specializing in business and management. This network offers our students the chance to broaden their perspectives, experience different cultural and academic settings, and expand their international connections. It’s an opportunity for you to dive into a new setting that equips you for a global career.
Student Life
Frankfurt School takes pride in its international student community. Many of our aspiring and inspiring individuals lead important initiatives such as fundraising for environmental causes or for tech and innovation start-ups as well as engaging in consultancy competitions, sports and wellbeing activities.
Career Services & Your FS Network
On completion of the Master in Applied Data Science, you will be qualified to connect the dots for businesses. Companies, including the Big Four, are seeking experts who understand specific wants and needs and can provide relevant solutions for genuine business transformations. Job opportunities will include but not be limited to Data Scientist, Business Analyst, Consultant, Cloud Engineer, AI Engineer, Software Engineer, Machine Learning Engineer, Product Owner and new roles in all sectors that are experiencing a digital transformation.
Build a strong network for your career with our exclusive corporate connections. Frankfurt School's Career Services team are here to provide you with individual consultations on business and management career paths. With our regular guest lectures and company visits, plus the opportunity to work part-time throughout your full-time studies, you are in the spotlight for employment after graduation. Check out the latest event with Adobe here.
Check out where our students are working!
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"Enrolling in MSc Applied Data Science at Frankfurt School has significantly transformed my career path, transitioning me from business to the IT industry. The curriculum provides a comprehensive blend of data science theory and practical insights into backend systems that manage data collection, ingestion, and transformation. This deep understanding of the entire data pipeline was instrumental in securing my current position at Amazon Web Services, where I contribute to cloud solutions. I particularly appreciate the 3-day model, which offers ample time to gain practical experience by working within the industry before graduating. This structure has also facilitated my early adaptation to the working culture in Germany, an aspect I find crucial for international students."
"MADS is one of those programs that actually delivers the interdisciplinary knowledge it promises. Learning about corporate finance and business strategy, along with practical coursework in deep learning and AI, is what differentiates me from my peers today; most see only one side of the equation. I took the unusual route from being a physicist to a data scientist, then a quant, and now an equity analyst. My job is to identify the most promising AI companies to invest. Having experienced the entire stack of data science technologies, from cloud databases to training large models, I feel very confident in picking companies with the right mix of technological edge and business economics. Another great aspect of MADS is its social environment. I met some incredibly talented and diverse people who are now lifelong friends."
"During my Master of Applied Science at the Frankfurt School of Finance and Management, the curriculum was instrumental in bridging my economics background with advanced technical skills. Courses in data analysis, programming, and financial modeling provided a comprehensive understanding of how to leverage data for strategic decision-making. This educational foundation has been directly applicable to my role as an Analytics Analyst at Deutsche Bank, where I utilize these skills to interpret complex data sets, develop insights, and drive data-informed decisions that enhance our financial strategies. Studying at Frankfurt School was an amazing experience, enriched by the incredible people I met. I formed valuable connections and made lifelong friends who have been a vital part of my personal and professional journey."
"I find that the technical requirements in the field of data science are very diverse and the Master in Applied Data Science programme was designed to train us “full stack” through application oriented projects. In addition, the management courses, and specializations such as AI ethics help me now with the strategic development of the data area in my company.
I got my dream job, so that‘s certainly a good example. Also, most of my fellow students could choose between positions. I would advise students considering enrolling in this programme to learn Python and participate in extracurricular activities. This way, you will be well-trained and will have already built a network."
"Starting a technical program without a technical academic background was challenging, but the MADS program guided me through the core basics of Data Science. Beyond learning various tools, I deeply understood the implications of handling data, including human biases and the sensitive aspects of data interpretation. This process opened my eyes to technical solutions in every corner of the world and business, significantly expanding my capability to understand both business and technology. The opportunity at Frankfurt School to work three days a week while studying and collaborating with big companies boosted my confidence for post-graduation work. I am currently working as a Cloud Consultant and Developer. Additionally, I had fantastic peers with whom I remain in close contact and can seek professional advice anytime."
"The 3-day study model is one of the unique advantages of the Frankfurt School Master programmes that really stands out for me. Not only does it give you the opportunity to earn something while studying, but you can immediately apply what you just learned in class. In doing so, you deepen your knowledge and work on a practical application. My working student position was an ideal opportunity for me to test whether I would like to go into strategy consulting full-time after graduation. However, over the two years of the Master programme, you can also get a taste of different industries to try out and hopefully secure some full-time offers in the process. In my experience, employers really appreciate the additional work experience, and it helps you stand out from the competition."
"The MADS Program and Frankfurt School Network empowered me to make significant career shifts. I transitioned from project management to data science and data architecture teams in Germany. The program's courses, led by experienced professors, provided a strong foundation in data science and machine learning concepts. It's a fantastic steppingstone for anyone exploring this ever-evolving field. However, the true value lies beyond the coursework. The MADS program is like Willy Wonka's Golden Ticket – it unlocks a network of exceptional people and knowledge. But like Charlie, it's up to us to capitalize on this opportunity by actively building relationships and pursuing further learning. This program has been transformative for me, and I highly recommend it to anyone seeking a career change in Germany."
“The MADS program at Frankfurt School has been a transformative experience for me, seamlessly bridging my business background with the rapidly evolving field of Data Science. What sets this program apart is the interdisciplinary approach, encompassing both theoretical knowledge and practical application of data-related concepts. This well-rounded education was pivotal in securing my current position as an Analytics & Insights Manager at Procter & Gamble, where I thrive to drive business growth by generating valuable consumer and product insights. Combination of academic learning and industry exposure offered by MADS is truly unparalleled and very appreciated. This is reflected in one of the most valuable features of the MADS program, namely its 3-day model, which gave me an opportunity to apply learned concepts quite quickly in a real-world setting. It enabled me not only to gain practical experience at a prominent German company, but also facilitated my integration in the German culture and working environment – a crucial aspect for every international student.”
"Studying Applied Data Science at Frankfurt School was an enriching experience. The program strikes a great balance between the various disciplines needed to excel at data science. The faculty's diverse expertise in academia, applied science, business, and entrepreneurship provides different angles to the concepts taught which helps acquire a more objective view on the whole. The courses are cutting-edge and very practical, which made them immediately useful in real-life situations. The program also attracts a diverse range of students, which adds a lot to the overall experience. I felt well-prepared and confident in my skills after completing the program. To anyone considering moving from business to data science: it is a lot of work but it's definitely doable and this is a great program to do so!"
"I am currently working as a Data Analyst and Data Scientist at ING Germany. My studies of Applied Data Science at Frankfurt School of Finance & Management have been highly effective and meaningful. The program offers numerous practical case studies of data science, providing me with ample career development opportunities, particularly at the intersection of finance and data science, which opens up many possibilities for growth. The entire curriculum is highly international, with students from diverse backgrounds, allowing me to appreciate and enjoy the importance of cross-cultural communication, which helped me with working in an international organization."
Employment statistics - Class of 2023
Starting Salary after Graduation*
Average Salary (including bonus): 75,300 EUR*
*Excluding trainees, interns.
Where do our graduates work?
Our Master in Applied Data Science alumni have secured jobs in a variety of companies and industries since graduating.
List of Employers
- Accenture
- Allianz Global Investors
- Amazon
- BASF
- Bayer
- Boston Consulting Group (BCG)
- CARIAD
- China Fortune Securities
- citiworks AG
- Commerzbank AG
- DekaBank
- Deloitte
- Deutsche Bank AG
- Deutsche Börse
- Deutsche Bundesbank
- Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
- DZ Bank AG
- Eurex
- EY
- flatexDEGIRO AG
- Fraunhofer IEE
- Genesis Motor Europe
- HSBC
- ING Germany
- JPMorgan Chase & Co
- KMPG
- Lufthansa Group
- McKinsey & Company
- Merck KGaA
- Miles & More GmbH
- QuantumBlack, AI by McKinsey
- RWE
- StepStone
- The Export-Import Bank of China
- The Walt Disney Company
- Wayfair
Employment by Industry
Employment by Function
Time until employment
Out of all the students looking for employment, 88% found jobs within 3 months of graduation.
Country of job entry
Class Profile
Overview
Number of Students: 35
Nationalities: 12
Average Age: 27
Origins of Students
Educational Background
Learning Experience
Our Master in Applied Data Science applies a practical approach to your studies by preparing you for the realities of data science in the working world. We do this by strengthening your statistical, mathematical, and computational skills and providing you with practical experience in the day-to-day operations of the workplace.
Teaching Methods
Frankfurt School emphasizes a student-centered, interactive approach to learning that promotes collaboration and engagement. Most other degree programmes prioritize in-class teaching, supplemented by online elements. At Frankfurt School, students actively participate in group activities such as presentations, simulations, and business games to foster peer learning and teamwork. Additionally, regular guest lectures provide rich opportunities to network with industry leaders and alumni, offering industry insights and relationship-building that are crucial for your future career. With a strong focus on effective problem-solving, students encounter real-world business scenarios, bridging the gap between theoretical knowledge and practical application.
Competitions & Awards
Frankfurt School encourages students to participate in various challenges and competitions, throughout the year, allowing selected students to prove themselves and compete against other top universities worldwide.
German Classes
The Master in Applied Data Science programme is conducted exclusively in English. However, German language classes are offered to support non-German speaking students throughout their enrollment. We highly recommend students learn German prior to arriving in Frankfurt to enhance their employment prospects.
Apply
Requirements
- Certified copy of your undergraduate transcript of records and degree award certificate
- Certified copy of your TOEFL / IELTS results or equivalent (TOEFL - 90 iBT, IELTS 7.0 or equivalent)
- Official GMAT score report, GRE score report or FS Admissions Test
- CV or resume (must be in English)
- Successful admissions interview
An application fee of 100 EUR applies.
*Please note: The BT Methods can be taken only once
Deadlines & Discounts
| Early Bird (EUR 4,000 discount)*, *** |
30 November 2024 |
| Early Bird (EUR 2,000 discount)**, *** |
31 March 2025 |
| Final Application and Scholarship Deadline | 30 June 2025 |
*In order to secure the Early bird discount you must have received an admission letter by 31 March
**In order to secure the discount you must have received an admission letter by 30 June
***Please be aware that internal applicants are not eligible for the early bird discount. Instead, they will receive an alumni discount.
We encourage you to complete your application as soon as possible as there are financial advantages for candidates who submit a complete application early.
Finance your Master in Applied Data Science
Your degree is an investment in your professional future. As a business school of international standing, not only do we offer you ideal conditions for earning a degree – we also offer you excellent career prospects.
Since we can guarantee the quality of our teaching and research, we expect and encourage the highest levels of commitment and motivation from our students.
Cooperations
FERI
The FERI Group is a bank-independent multi-asset investment company that develops customized investment solutions for institutional investors, family assets and foundations. The part-time position is in the Investment Client Management department, which is responsible for the preparation of investment proposals, the support of tenders and due diligence questionnaires, as well as individual pitches for private and institutional clients.
FERI covers 50 % of the tuition fees. Students work three days a week at FERI with a salary of at least €1,900/month. The requirements are German language skills at C1 level, an interest in the operational fund business and a good Bachelor's degree in economics or business administration. Please find the full job advertisement here. You can apply directly to FERI via the link in the advertisement.
Proresult
Proresult is a financial service consulting company with projects in Frankfurt. For students with a background or interest in financial consulting and C1 level German skills, this is a fantastic opportunity to gain experience while studying. The cooperation guarantees a two-year part-time paid position at the company (3 days a week). In return, Proresult covers tuition fees in full.
One cooperation opportunity will be offered per intake. The selection process considers academic excellence, personal and professional achievement as well as performance in the assessment process. When applying for this cooperation, applicants agree to share their data with Proresult for the selection process. Job interviews and the final selection of a candidate for the cooperation will be conducted by Proresult.
Candidates should complete the cooperation application as part of their online application. Find out more here.
Contact
You can contact Proresult directly if you would like to know more about the position.
Contact:
Andreas Peters
Contact
Tamay Kasap Ercanli
Recruitment Officer
Hana Karlovic
Recruitment Officer
