From Africa and agile working to river power plants and flowing data

Michael Straif, valantic

Michael, can you tell us something about your current position at valantic?

Sure, I am currently working as a Principal Consultant AI. My job is to support customers in establishing AI technologies in their company. This includes developing AI strategies, identifying use cases, implementing them and training employees.

What has your career been like so far?

I’m originally from Austria and studied at the University of Natural Resources and Life Sciences in Vienna. During my studies and my time as a research assistant, I developed an interest in statistics on ecological systems and forecasting models, especially in the context of water management issues. This led me to the field of data science, which I deepened during my studies and research activities. After my studies, I worked for about eight years as a research assistant at the Department of Soil Science. In addition, I often travelled to Africa as part of my university employment, supervised African students there and was involved in an EU framework project.

What did you learn in Africa, what had a particular impact on you?

During my time in Africa, I worked on an EU framework project, which allowed me to experience different working approaches and cultural differences. It was particularly impressive that despite the different approaches, similar results were ultimately achieved. This experience taught me to take a step back, to be open to new ways of thinking and to develop respect for other ways of working.

Did you take anything away from the way of working in Africa?

Definitely, especially in the area of Scrum and agile working. The willingness to deviate from fixed structures and try out new approaches, as well as a certain patience when dealing with bureaucracy – these are things that I took with me from my time in Africa.

What do you find particularly exciting about your job?

My passion for data science comes from my technical training with a lot of maths, physics and chemistry. The fascination lies in explaining processes through data, visualizing the world with mathematical formulas and making predictions. Data science enables me to understand challenges, find new solutions and make predictions about the development of problems. One example of this is the evaluation of the ecological functionality of artificially created habitats in a Danube river power plant in Austria, which I analyzed as part of my dissertation.

You’ve been working in the field of data analysis for a very long time now. What can you pass on to younger colleagues?

That’s an important question. For young colleagues working in the field of data analysis, it is crucial to understand that algorithms play a supporting role in the analysis, but are not the only element. Much more important is the technical knowledge of the data itself.

A deep understanding of the data, often referred to as feature engineering, is essential for data scientists, as focusing too much on algorithms can only lead to partially valid results. It is not enough to rely on mathematical models without ensuring that the content is correct. A good data scientist is characterized by the correct interpretation of the data and results and creates well-founded recommendations for action based on this, whereby a constant sharpening of the understanding of the content is crucial and not just the use of tools.

Michael, that was an exciting insight into the world of data analysis. Thank you very much for your insights.

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