With mathematical precision and a passion for data, Olivia Pfeiler is helping to shape the future of artificial intelligence. A graduate of the University of Klagenfurt, she studied Engineering Mathematics and Romance Languages and Literature, before going on to complete a PhD in Engineering Mathematics. She has been working for many years at KAI – the Competence Centre for Automotive and Industrial Electronics in Villach, where she now heads the data science team as Head of Data Science. In this alumni portrait, she talks about her career path, the future of data science and AI, and the importance of women in technical professions.
You studied Engineering Mathematics and Romance Studies at the University of Klagenfurt and subsequently completed a PhD in Engineering Mathematics. What prompted you to choose this combination of subjects back then?
The combination wasn’t planned at all originally. I started with Engineering Mathematics and, whilst studying, attended a three-week language course in Spain through the university. I went there without knowing any Spanish and was determined to carry on afterwards. That’s why I enrolled in Romance Studies – initially just so I could learn Spanish intensively. It turned out to be much more than that: I completed my bachelor’s degree and spent two semesters abroad in Spain and Nicaragua. For me, Romance Studies was always a personal interest and a nice balance to mathematics – never a strategic career decision.
When you look back on your time as a student at the University of Klagenfurt, which experiences, courses or people had a particular influence on you?
What I remember most is the close-knit atmosphere. Both Applied Mathematics and Romance Studies were relatively small degree programmes. We knew the other students as well as the lecturers, which led to very personal interactions. In Mathematics in particular, we quickly organised ourselves as a group and supported one another. Of course, the course wasn’t always easy, but we motivated each other and said together: ‘We can do this.’ That sense of camaraderie really shaped my time at university.
Today, as Head of Data Science at KAI, you lead a team in the field of data science. How did your journey unfold from your PhD to this leadership role? Was there a decisive moment in your career?
My career path was rather organic – there wasn’t one single decisive moment. I was constantly presented with new challenges and usually said: ‘Yes, I’ll give that a go.’ So, over time, one thing led to another. When I started at KAI, there wasn’t yet a dedicated data science team. I was the first person in this field and helped build it up step by step. Eventually, the team was big enough to become a separate unit, which I took charge of. My colleagues and line managers, who supported and encouraged me, were also key to this.
Data science thrives on numbers and algorithms – which human qualities are at least as important in your day-to-day work?
Technical understanding is, of course, the foundation. In my role as a manager, however, communication, openness and trust are at least as important. My team consists of 20 people – for us to achieve ambitious goals together, we need an environment where we can speak openly with one another, give feedback and trust one another.
The human element also plays a crucial role in the development of data science and AI solutions. We need to understand what users really need, where their problems lie and how we can build trust in new technologies. Even the best technical solution is of little use if it is developed without taking people into account.
Data science and artificial intelligence are currently on everyone’s lips. How has this professional field changed since you started your career – and what developments do you expect to see in the coming years?
Above all, perceptions have changed. The foundation of data science remains statistics – and that, of course, existed long before terms such as ‘data science’ or ‘AI’ became popular. At the same time, enormous computing power, new data models and, in particular, language models such as ChatGPT or Gemini have opened up entirely new possibilities.
I believe that the current hype surrounding generative AI will die down somewhat, and the focus will shift more towards the question of where these technologies can be applied. I find the combination of AI with knowledge of physics particularly exciting. This can make models more robust and reliable – for example, in materials research or in the optimisation and virtualisation of industrial production processes.
KAI combines cutting-edge research with industrial applications, particularly in the field of semiconductor technology. What are you and your team working on, and what fascinates you most about this field of research?
We are looking at how data science and AI can be applied in semiconductor research and production – for example, in the automated detection of defects, the analysis of large volumes of data, or the virtualisation of production processes. What fascinates me most of all is the interplay between research and practical application. Using our methods, we can help to better understand complex interrelationships and make a very resource-intensive production process more efficient. There is enormous potential for this in semiconductor technology.
You volunteer as a Women in Data Science Ambassador. Why is this commitment important to you, and what contribution can such networks make?
Diversity is hugely important, particularly in the fields of data science and AI. Algorithms learn on the basis of data and are increasingly making automated decisions. If only very homogeneous groups are involved in their development, blind spots can easily arise. Different perspectives – whether in terms of gender, age, cultural or social background – help to develop better and more balanced solutions. Networks such as Women in Data Science raise awareness of this and, at the same time, make women in this professional field more visible.
Women remain under-represented in technical professions and leadership roles. What has been your personal experience, and what do you think is needed to inspire more young women to pursue these careers?
Role models play a vital role. When young women see other women working successfully in technical professions or leadership roles, this career path becomes more tangible for them too. We see this time and again at Women in Data Science. At the same time, it is not enough simply to inspire women to pursue technical training. It is also crucial to create a working environment in which they want to stay in the long term and see opportunities for development. This includes an open working culture, fair career prospects and conditions that enable both women and men to balance work and family life equally.
Innovations often arise from the interaction between academia and industry. What role does the collaboration with the University of Klagenfurt play in KAI’s work?
Our collaboration with the University of Klagenfurt is very important to us. Together, we supervise Master’s theses, collaborate on research projects and offer students work placements and practical experience. This benefits both sides: scientific findings find their way into industrial applications, and we come into contact with qualified young talent at an early stage.
If you were to study again today, would you follow the same path, or is there anything you would do differently?
I would study mathematics again, choose the University of Klagenfurt once more, and also opt for the combination with Romance languages. If I were to do anything differently, I would pay more attention to the business-related subjects during my degree. I now realise how useful this knowledge is, particularly for leadership roles.
What advice would you give to today’s students – particularly those aiming for a career in STEM subjects?
Think outside the box! In STEM professions, you almost always work in an interdisciplinary way. That’s why it’s important to be able to explain your work clearly to people outside your own field of expertise. And don’t be afraid of making mistakes. In research and development in particular, not every experiment works. The key is to deal openly with mistakes and feedback, learn from them, and carry on.
To conclude, let’s look to the future: where do you see the greatest opportunities, but also the greatest challenges, for data science and AI over the next ten years?
AI offers enormous potential for automating processes, making knowledge more readily accessible and accelerating innovation. The challenge will be to keep pace with the sheer volume and speed of developments and to identify which technologies we can best deploy where.
At the same time, the role of humans will change. As AI increasingly processes data, takes over processes and provides the basis for decision-making, we must interpret the results correctly and use them responsibly. It will be crucial to build trust in these systems without having to manually check every single step.
 

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Der Beitrag “Artificial intelligence needs diversity” erschien zuerst auf University of Klagenfurt.