Artificial Intelligence Studies

Interview With Lenny Klump

Lenny Klump is a Data Scientist at our Berlin office, where he works on large-scale projects for internationally renowned clients.

In this interview, we spoke with him about his path into Data Science. He explains how his bachelor’s degree and an influential professor shaped his decision to pursue a master’s degree and helped define his professional goals.

He also shares practical advice for students just starting out and explains how to prepare effectively for entering professional life.

What did you study in your bachelor’s and master’s programs, and why did you choose those fields?

I studied Business Informatics for my bachelor’s degree because I’ve always enjoyed solving problems and tackling complex challenges. What fascinates me most about computer science is the systematic breakdown of problems and the development of solution paths—often implemented in code. At the same time, I was very interested in digital applications and products, but not just on a theoretical level. I wanted a clear connection to real-world practice.

Business Informatics was the perfect combination for me: solid theoretical foundations in mathematics, computer science, and technology, paired with concrete applications in a business context.

For my master’s degree, I moved into Data Science. One early influence was the father of a friend who works at Microsoft and recognized the importance of this field very early on. Another key factor was a statistics professor during my bachelor’s studies who didn’t just present the material in a dry way, but instead used computers and programming early on to demonstrate the enormous potential behind the methods.

“What really fascinated me at the time was the idea of using data to quantify the future and, to some extent, make it predictable.”
— Lenny Klump, Data Scientist at Alexander Thamm [at]

After finishing my bachelor’s degree, I completed an internship in Data Science. It showed me just how much potential the field holds and how much I enjoy the practical work. From that point on, it was clear to me that I wanted to deepen my expertise in this area.

What were the highlights of your studies that you remember most fondly?

My absolute highlight was that I spent almost my entire master’s program abroad. I studied in Sophia Antipolis in France and in Eindhoven in the Netherlands. Beyond experiencing different university systems, I mainly got to know people and cultures from all over the world.

I would strongly recommend studying abroad—or at least doing an Erasmus semester—to anyone who has the opportunity. You meet incredibly smart people from many different countries, become more open and independent, and develop a new perspective on many things. That time had a lasting impact on how I view studying, working, and life in general.

Did you work or complete internships alongside your studies? If so, where, and what were your experiences?

Starting in my third semester, I consistently worked alongside my studies and completed internships. I think the German working-student system is excellent, because you quickly find out whether the field you chose actually suits you in day-to-day work. You also gain insight into areas and specializations you might not have considered otherwise.

During my bachelor’s degree, I worked as a student assistant at Deloitte, where I gained my first experience in consulting. It gave me valuable insight into project work, client interaction, and how large organizations operate.

Between my bachelor’s and master’s degrees, I completed a Data Science internship at Performance One Brain. There, I worked on more complex data projects for the first time—projects that also had to be commercially viable. Those experiences played a major role in my decision to pursue a master’s degree in Data Science.

During my master’s program, I continued working at Performance One Brain as a student employee in Data Science.

I later wrote my master’s thesis at Vector Informatik. Practical relevance has always been very important to me, so I wanted to collaborate closely with a company and work on a topic that was not only theoretically interesting, but also directly applicable.

Do you feel that working alongside your studies helped you later in your career? If so, how?

Absolutely. Practical work during my studies was extremely helpful for my professional career. You learn how companies function, how projects really unfold, and how decisions are made. Most importantly, you learn how to work with colleagues and clients—things that don’t come up much in everyday university life.

At the same time, it helped me understand which academic content would actually be relevant later on. When you’re working on a project and suddenly realize that methods or concepts from a lecture are directly applicable, they stick with you much more strongly. These experiences made my transition into professional life much easier, because many things already felt familiar.

What challenges did you face during your studies, and how did you overcome them?

Especially at the beginning of my bachelor’s degree, the biggest challenge was feeling overwhelmed. Suddenly, no one was checking in on you anymore. There was much more freedom—but also much more responsibility. I had to learn how to organize myself, manage my time, and prioritize what really mattered. That took time and involved a lot of trial and error, countless to-do lists, and some poor planning decisions that I learned from.

Another challenge was group work and project-based assignments. People come together with very different backgrounds, working styles, and expectations. Over time, I learned how important clear communication is—and how helpful it is to define roles early and openly discuss individual strengths. That’s how you end up with results everyone is satisfied with and where everyone feels valued.

At what point did you know what you wanted to do professionally after graduating?

That realization came around the same time I decided to pursue my master’s degree. In statistics lectures and later during my internship, I realized that Data Science truly excited me—both theoretically and practically. From that point on, I knew I wanted to work in this field after graduation.

Of course, during my master’s program I explored additional specializations that I liked more or less, but overall they helped clarify my direction even further.

How did you find your current job at [at], and what criteria were most important to you when applying?

I found my current position at [at] through job postings and the company’s website. I was already familiar with the name from my bachelor’s degree, having encountered it at the IKOM career fair at TUM.

For me, the most important factor was the people. You spend a significant portion of your life at work, so it has to feel right on a human level and be professionally inspiring. At [at], I had the impression that I would get along well with my colleagues and learn a lot from them.

In addition, company culture, location, and the opportunity to work on exciting Data Science projects were key reasons why I applied.

What was the application process like?

The application process was very transparent and well structured. I applied through the website and was invited shortly afterward to an initial interview with the People team. The focus was on aligning expectations on both sides, and I also had the chance to ask questions and get a first impression of the company.

Next, I received a forecasting case study and had about a week to work on it. I prepared a presentation, which we then discussed in detail during a technical interview.

Finally, there was a concluding interview with two potential team leads. That conversation focused heavily on my possible development path at [at]—including growth opportunities and what my role could look like in practice.

What does your day-to-day work at [at] look like now, and what have been your highlights so far?

At the moment, I’m working in parallel on two client projects, which naturally take up most of my time. In addition, I regularly try to contribute to knowledge sharing—for example through internal formats or blog posts like this one.

Together with two colleagues, I also helped launch an initiative to organize after-work events at the Berlin office at least once a month.

My personal highlights include the openness of my colleagues and how quickly I felt integrated. Another major highlight is the content of my work itself. The projects are technically challenging, and I get to work with cutting-edge technologies—currently especially in the agentic AI space.

What advice would you give to students considering a similar academic and career path?

I would advise students not to panic if they don’t have a very specific job in mind at the beginning. If the general direction feels right, that’s already a great starting point. Through studying and practical experience, you’re exposed to so many different specializations that you have plenty of time to discover what really suits you.

I’d also recommend gaining as much practical experience as possible—whether through working-student positions, programming projects, hackathons, or online challenges. This helps solidify knowledge much more effectively and quickly shows you what you enjoy and what you don’t. If possible, I’d even suggest extending your studies slightly in favor of gaining more hands-on experience.

Special thanks to Lenny for his time and for sharing his helpful tips for future AI and data experts.

Bachelor’s Programs In AI And Data Science (DACH Region)

The following overview presents twelve selected bachelor’s programs in Artificial Intelligence (AI) and Data Science offered by universities in the German-speaking region (DACH). The focus is primarily on on-campus programs, complemented by a small number of high-quality online and distance-learning options.

All programs provide a solid foundation in computer science, mathematics, statistics, and machine learning, while offering different emphases on theory, practical application, or interdisciplinary approaches.

The table is intended as an initial point of reference for prospective students who are interested in pursuing an academic path in data-driven technologies.