The world runs on data. Every day, human beings produce more data than they did for the vast majority of human history, and the ability to collect and organize this data has revolutionized how we approach problems in a wide variety of different contexts. Data science is the technical and professional process of creating the systems and infrastructure needed to take this immense amount of data and translate it into something useful for practical applications. Data science is employed in almost every professional field, so if you are interested in learning a skill set that opens the door to high-paying, in-demand career opportunities, learning the fundamentals of data science is a good way to start.
Learning data science involves learning several skills that are commonly used in data-related tasks, regardless of what kinds of data are being utilized. These skills include the Python programming language, Excel, SQL, and various soft skills related to data analytics. This training will help you get a practical understanding of how data is utilized, how data scientists store and organize data, and what practical applications there are for data science tools.
Key Industries for Data Science Professionals in Germany
Germany’s strong economy and broad digital transformation have created a robust market for data professionals. Since data science is utilized in virtually every major field (across public and private sectors), as the economy grows, so too does the demand for skilled data experts. Major industries driving demand include:
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Automotive & Manufacturing: Germany’s automotive giants (e.g., Volkswagen, BMW, Daimler) and engineering firms are leveraging data for R&D, production optimization, and autonomous systems. These companies use data science tools for designing new features for their cars, improving the effectiveness of their marketing campaigns, and driving down the cost of their cars with data-driven initiatives to make the production process cheaper. Data science is also a vital part of the R&D process, since the models that data scientists create can help streamline the development process by creating data-driven, real-world computer simulations for testing changes before moving them to physical testing.
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Finance & Insurance: Banks and insurers (e.g., Deutsche Bank, Allianz) hire data scientists for risk modeling, fraud detection, and analytics, with the finance sector offering some of the highest salaries. Data is essential to these industries since it allows them to make informed decisions about the state of the markets, and it lets them make predictions about where the market will be in the future based on accurate historical trends. The credit and insurance industries are almost entirely built on risk modeling, and these new technologies make access to these services far better than they had been in the past.
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Technology & Telecom: Companies like SAP, Siemens, and Deutsche Telekom are continually expanding their data/AI teams. These companies are constantly looking for professional data science experts who can develop and design new ways to gather, clean, and handle large amounts of data. With the increased investment in artificial intelligence and machine learning technologies, data-related projects are at the cutting edge of the tech industry, and they represent the kinds of work that companies are funneling money introduction to develop the next big breakthrough in data science.
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Retail & Ecommerce: Large retailers and online platforms analyze customer data (for example, Zalando in ecommerce employs many data scientists). This is done to give businesses of all sizes a better understanding of what aspects of their projects are working and which need to be re-examined. It also lets them perform more targeted explorations of their consumer base and make future predictions for sales based on historical records. Retail roles tend to lean more towards the analytics side of the field, but they heavily utilize data collection and analysis tools designed by data science experts.
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Healthcare & Pharma: The pharma and medical devices industry (and growing health tech startups) use data science for research and personalized medicine. Not only do hospitals and providers use data science tools to organize, share, and protect sensitive patient data, but the rise of artificial intelligence is poised to change diagnostic medicine as a field. Researchers and data scientists are working on training AI tools to read personal medical files and make predictions and diagnoses based on billions of data points, ideally leading to better preventative care, earlier detection of ailments like cancer and genetic predispositions, and making the diagnostic process significantly more accurate.
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Public Sector & Research: Government initiatives in AI and numerous research institutes (e.g., Fraunhofer, Max Planck) also contribute to the demand for data expertise. This includes everything from gathering data on constituents and citizens for demographic reasons to working on campaigns and microtargeting specific potential voters based on complex computer models. If you are interested in the research side of data science, there are almost always new projects starting up at universities and specialized research institutions.
Common Job Titles and Typical Salary Ranges for Data Science Professionals in Germany
There are multiple professional roles that are filled by trained data science professionals. Here are a few of the most common job titles and their typical salary ranges in Germany.
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Data Scientist: Data Scientists are responsible for building and maintaining the tools and applications that professionals use to gather, clean, and analyze data. They handle the theoretical side of data management, and they tend to work on industry and project-agnostic data processes. While an analyst is working with, for example, a company’s finances to help make informed decisions about the direction taken with their investments, they will be using the tools created by a Data Scientist.
- In Germany, data scientists earn a typical salary between €49,000 and €68,000(mid-career average ~€57,000)
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Data Engineer: Data engineers work on building and maintaining the infrastructure in which data is stored and accessed. They are usually hired to work in fields that require a significant amount of data storage power, like healthcare or government. These professionals are tasked with ensuring that data is clean, organized, and easy to access so that scientists and analysts can easily perform their job functions and so that administrators and other non-data specialists can also easily access data.
- In Germany salaries for data engineers range from €48,000 to €66,000 (average ~€55,000)
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Machine Learning Engineer: Machine learning engineers are specialized data scientists who work on projects related to AI and machine learning algorithm training. They can be working in a generalist capacity (working on developing the next generation of AI technology) or applying current AI technology to specific tasks such as writing algorithms for the healthcare or insurance industries. These professionals are well compensated, given the training required to become proficient with machine learning algorithms.
- In Germany, machine learning engineers earn between €52,000 and €92,000 (median ~€65,000)
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Data Analyst: These professionals handle the practical side of data projects. Unlike data scientists who develop tools and applications, analysts apply those tools and applications to real-world datasets with practical desired outcomes. For example, a Data Scientist may design a tool for tracking customer engagement with online ads, and an analyst will use that tool to track engagement with a specific ad campaign to determine what is and isn’t working.
- In Germany, a Data Analyst can expect to earn between €44,000 and €60,000(average ~€50,000)
