“Jacek Haußner feels like a detective. "I look for connections in data that aren't obvious at first glance, and then draw conclusions from them," he says. Haußner is 41 years old and works as a data scientist in Frankfurt. He has been self-employed for two years, advising companies on how to achieve greater success through data science and specialized software.
The term "data scientist" translates literally as a researcher or scientist who works with data. It is a field one can study at university; there are over 120 bachelor’s degree courses and more than 140 master’s programs in Germany. However, there are other ways to become a data scientist—for instance, by completing further training in data science through the Chamber of Industry and Commerce (IHK) if you are already an engineer or IT specialist. Alternatively, one can enter the field directly through internships or project work.
That is how Haußner found his way into the profession. He initially studied Applied Computer Science—specializing in Business Informatics and Electronic Business—in Fulda. After graduating, he worked as a software developer for two management consultancies. Some of his projects involved "Big Data"—datasets so vast, complex, or rapidly changing that they could no longer be analyzed using conventional software. "Back then, the term 'data science' wasn't yet established, though today that’s what we’d call the project," Haußner explains. "Our goal was to derive new insights from the data using algorithms and statistical analysis."
He took an immediate liking to working with data. "I was curious even as a child." "I find it both challenging and rewarding to get to the bottom of complex questions and derive practical insights from them." He cites two examples of this kind of "detective work" as a data scientist: arranging seating during aircraft design to maximize airline revenue—such as determining the right mix of economy, business, and first-class seats to ensure passengers have enough space while allowing the airline to sell as many tickets as possible—or estimating whether supermarket customers are pregnant, even when they aren't buying products typically associated with pregnancy. "Depending on the industry and the specific role, data science offers unique projects, so it never gets boring," says Haußner.
He founded his company, Eternal Labs, two years ago. He is currently working on a major project for a large client as well as three internal projects, one of which involves developing a digital application for mental health. "At my previous employer, I built a data science team and the corresponding department from scratch. With my own company, I want to focus more on my own ideas and continue my personal development. I see this as a great opportunity, but also a challenge." Haußner works almost exclusively from home. "It allows me to work with greater focus and use my time more effectively. Besides, working from home has long been the norm in the IT sector. My project teams are often spread across multiple locations, and we collaborate primarily in a digital environment." However, he does regularly organize on-site workshops "to help the team bond."
An important part of his job involves managing expectations regarding data science. "People often have the idea that you can quickly arrive at clear-cut answers based on the notion that 'we have AI now, so all our problems are solved.' But that’s not how it works. The foundation for reliable results is a clear understanding of the problem, the data, and the objectives. These aspects must be carefully aligned with one another." "That’s where data science offers immense added value." When he talks to non-experts about his profession, he often finds that people equate data science with AI. Yet, data science involves far more than just developing complex algorithms. "It’s about asking the right questions, understanding data within its context, and explaining results in a way that makes sense to others. Many people are surprised by just how significant the communication aspect is."
According to Hays, one of the world's leading recruitment agencies, data scientists in Germany earn an average gross annual salary of €58,100. One in four earns more than €86,500. As the managing director of his own company, Haußner pays himself a gross annual salary of around €100,000. "While I can help determine the terms of my own employment contract as a founder, my salary has to stand up to an arm's-length comparison; it cannot be disproportionately higher than what is customary in the industry." As a married father in tax class IV, he is left with a net annual income of €55,000 to €65,000. At his first job after university, he earned €45,000 gross; since then, his salary has steadily increased. "That is primarily because I have continuously developed my skills and proactively taken on more and more responsibility." He considers his current salary appropriate. "I work hard and bear a great deal of responsibility as an entrepreneur."
He usually starts at six in the morning. "That’s when I’m alert and focused; I’ve already done some work, so by the time the first team meeting rolls around at nine, I can clarify questions and resolve any issues that have come up." He spends the rest of the day working on client projects, sometimes via online meetings and sometimes through on-site visits to the client. In the evenings, he handles his duties as managing director—coordinating with his team or acquiring new clients, for instance. The father of two works 50 to 60 hours a week, noting, "I regularly work evenings and sometimes weekends, too. But I try to keep weekends free for my wife and children, as I consider family time very important."
Göran Kauermann believes the general public isn't familiar enough with the data scientist profession. He is the Director of the Institute of Statistics at Ludwig Maximilian University in Munich and President of the non-profit German Data Science Society. "Data has played a crucial role in our lives for decades—and increasingly so with the advent of AI," he says. "Few people realize that data scientists are essential for aggregating and managing data, as well as for optimizing processes in both everyday life and the corporate world." Because the field of data science is constantly yielding new insights, he is also actively involved in the society: "We want to bring together education, research, and practical application."
Kauermann also has a tip for those just starting out who are interested in the job. "It’s not just about math and statistics; you need a broad skill set. A genuine interest in and basic understanding of mathematics are just as important as an enthusiasm for working with computers, communicating, and finding pragmatic solutions." He also advises giving careful thought to where you choose to study. "At some universities, the focus is on statistics, while at others it is on algorithms. It is therefore worth taking a look under the hood before deciding where to study, to ensure the training aligns with your own interests." [1]
1. So viel verdient ein Data-Scientist: Jacek Haußner berät Unternehmen, wie sie erfolgreicher werden können. Sein Gehalt findet er angesichts seiner Arbeitszeit und Verantwortung stimmig. Frankfurter Allgemeine Zeitung; Frankfurt. 21 Mar 2026: 31. FELICITAS WITTE
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