果冻传媒

With a father who was a mathematics teacher and a high school background in science and technology, it鈥檚 no surprise that Daphne is now working as a data science & AI expert. However, her path was not straightforward. From the very beginning, her motivation was to make a difference in healthcare. Daniel Kapitan (DK) spoke with Daphne in early 2025, just a few weeks after she had registered as an independent consultant at the Chamber of Commerce.

DK: Great to speak with you again! How does someone with a dental school foundation become an expert in data science?

"I started my career as a physiotherapist after first completing a dental school foundation year. I quickly realized that I wanted something beyond direct patient care. I wanted to work more with numbers, which, in hindsight, has been the common thread in my career. Through a master鈥檚 in Health Policy and Management, I moved closer to what I truly wanted. I鈥檝e now been working in the health insurance sector for over 15 years."

DK: What led you to enroll in our program?

"Five years ago, I joined the Safety Affairs department at VGZ, focusing on tackling healthcare fraud. From the start, I noticed that working with reported cases only revealed the tip of the iceberg and that data could help us detect far more fraud risks. That鈥檚 why we established a data team to analyze and predict fraud risks using AI and data. It was one of the first teams in health insurance companies to do so. Even back then, I knew I wanted to take this program because I foresaw that data would play an increasingly important role in healthcare. I was inspired by a friend who worked at Efteling and told me about a project with a data science EngD student optimizing visitor flows in their app. Eventually, I waited until our youngest child started elementary school. Then I thought: now it鈥檚 my turn!"

DK: Were you able to apply what you learned right away?

"Absolutely! By the time I started the program, I was already the product owner and lead of the central data team. One of the first things we learned was the importance of structured workflows, following the CRISP-DM model (a standard methodology for machine learning). I could immediately apply this in my daily work with the team. During the lectures, we were constantly reminded of the importance of properly understanding a problem before diving into data analysis. I recognized this in our own work as well. Additionally, I quickly became comfortable discussing which models or algorithms were most suitable for specific challenges. As a result, I was soon seen as an expert within my organization. I became the go-to sparring partner for data scientists and was frequently involved in discussions with the management team and even the board of directors."

DK: I firmly believe that almost anyone can become a data scientist. Like Chef Gusteau in Ratatouille says: "Anyone can cook."

"That鈥檚 exactly how I experienced it. When deciding whether to take the expert track, I spoke to several lecturers. Pieter Overdevest put it perfectly: 鈥楾he only difference between you and me is the number of flight hours.鈥� It鈥檚 absolutely doable, as long as you have an affinity for it and are willing to put in the effort. The support from the instructors and their confidence in me also played a huge role. Interestingly, this was the first program where I actually had to commit to the prescribed study hours. In my previous studies, that was much less the case!"

DK: You鈥檝e recently started your own business鈥攚hat are your plans?

"As a freelance consultant, I want to help healthcare organizations become more data-driven. One of my key motivations is to use algorithms in a positive way. After years of working in fraud detection and control at DSW and VGZ, I鈥檓 excited to now support healthcare providers directly. I see many healthcare organizations hesitating to take the first step. But I believe you don鈥檛 need to start with something groundbreaking鈥攋ust begin. Step by step, you can build the necessary knowledge and skills within the organization. Even without complex algorithms, there鈥檚 already so much to gain by approaching things more analytically. My goal is to show organizations what鈥檚 possible with data鈥攚ithout making it overwhelming."

DK: What trends or technologies interest you in achieving this?

"I think privacy-enhancing technologies, such as multi-party computation (MPC), hold great potential. MPC allows calculations to be performed on data without actually 鈥榮eeing鈥� the data. For example, in fraud prevention, it's crucial to understand the financial flows within an organization. Fraud detection becomes much more effective when you can get a complete overview of these financial streams. MPC could help conduct these analyses more effectively while ensuring business-sensitive information remains protected. Similarly, it could enable safer analysis of patient data across multiple healthcare institutions, giving a more holistic view of patient health without compromising privacy."

"In my work as a consultant, I also want to focus more on data visualization to effectively communicate insights and results. I鈥檓 excited to explore this area further and experiment with tools to create compelling visuals. In a way, it's a return to my creative side."