From psychology to data — without leaving people behind

I started in Psychology because I wanted to understand why people do what they do. Somewhere between my statistics courses and my first real dataset, I realised that the “why” questions I cared about increasingly had quantitative answers — and that I enjoyed finding them. That led me to a Data Science minor during my BSc, and eventually to the Behavioural Data Science track of the Psychology MSc at the University of Amsterdam.

The combination has shaped how I approach analysis. Psychology taught me to be careful with claims about people: behaviour is contextual, measures are proxies, and an average can hide more than it reveals. Data science gave me the tools to act on that care — to model behaviour over time, test predictions out-of-sample, and separate signal from wishful thinking.

The problems that pull me in sit at the intersection of data, UX, marketing, and digital products: why users disengage, which behaviours precede churn or dropout, how people with different levels of expertise navigate the same interface, and what content actually resonates with an audience. During my internship at De Innovatiespotter I’m seeing how much stronger an analysis becomes when engagement data and user research are read together — that experience has made the direction of my career very concrete.

I’m also genuinely curious about AI — less about the models themselves than about what happens when people use them. Human-centred AI, and the question of how analytical tools can support rather than replace human judgement, is a theme I expect to keep learning about for a long time. I follow new analytical technologies closely and enjoy figuring out which ones actually help.

What I want from the next few years is simple: real problems, good colleagues to learn from, and work where the analysis ends in a decision — not a slide that nobody acts on.