Do you dream of a career in data science? You're not the only one. More and more students are completing studies in Data Science, AI or Business Analytics and want to work as data scientists right away. But when you check job openings, you see one thing: there are almost no junior data scientist job openings, especially in finance. Why is that? We spoke to recruiter Julien about how data science works in finance, why "data scientist" is often not a starting point but an end station, and how you do start your career in data smartly.
Data science in finance: why it works differently
Many first-timers think that working as a data scientist means getting straight to work with AI and machine learning. In theory, that's true. In practice - especially in finance - it works differently. Banks and insurers work with enormous amounts of data, but are not allowed to do just anything with it. Regulators such as the Dutch Bank (DNB) and the European Central Bank (ECB) want to know exactly how each model works.
"In finance, AI is often still a black box," Julien explains. "And so it's not allowed. Everything a bank does has to be explainable and verifiable. You can't just run an algorithm and say, 'trust us'." Therefore, in finance, a well-coordinated collaboration between data specialists such as engineers, analysts and stewards is needed to deploy AI and data science effectively and verifiably. Consider:
- Data stewards ensure that data is properly collected and usable.
- Data engineers build the technical infrastructure and make sure everything is stored securely.
- Data and BI specialists analyze trends and build dashboards that give managers and supervisors quick insight.
Only when all these components are in place can a data scientist develop predictive models that are actually applicable in a regulated environment. And even then it's not just about building smart models, but especially: being able to explain how such a model works, why it's right, and what the impact is.
Wondering what it's really like to start your first job as a data engineer at a bank? 👀 Check out the story of Andrea, who works as a data engineer at ABN AMRO and tells how her working day looks like!
Why no junior data scientist jobs?
A data scientist is not someone with one specific task, but someone who oversees the whole picture: from data collection and analysis to modeling and business impact. And that's exactly why you see almost no junior data scientist roles. "Many organizations themselves don't yet know how they want to use data science," Julien explains. "People are still pioneering. And for pioneering you need experience. "
Moreover, the role of data scientist is still evolving. In one company you focus mainly on process optimization, in another on document recognition or fraud detection. Sometimes it's called "data scientist," sometimes "process mining analyst," and sometimes something completely different. The result? Many companies are looking for experienced people to help them make these technologies at all workable. And that makes it tough for startups.
So how do you get started?
Do you eventually want to work as a data scientist? Then it helps to broaden your view. It's not about that one job title; it's about gaining real-world experience with data. Consider roles such as:
- Data analyst (in finance) - you learn how data influences business decisions.
- Data engineer - you develop the infrastructure on which models run.
- BI specialist - you translate data into dashboards and insights.
But also: business analyst or reporting analyst can be good intermediate steps, especially if you are strong in communication or connecting people and data."Not everyone needs to be hardcore in programming," says Julien. "Maybe you're just good at presenting, persuading, explaining. Then you can be the bridge between the tech and the business - and that's just as important. "
Whatever role you choose, make sure your resume shows what you have to offer. 💪 Check out our 10 tips for a good resume here.
Data science: unpredictable yet promising
Fair is fair: no one knows exactly where the field is going. What's called "data scientist" now may have a different name five years from now. Developments are rapid. But one thing remains certain according to Jorn:"You can be as technically savvy as anyone else - if you can't explain it, it's no use to anyone."
This is precisely why soft skills are so important. Knowing what you are doing is step one. Explaining why it is relevant, and including people in your story - that's where you make the difference.
Conclusion: build your career from practice
Want to work in data science? Then start smart. Choose a role where you gain experience with data processing, learn about the financial sector, and develop yourself in the direction that suits you. There is no standard route, and that is precisely your strength. Start with a role where your skills come into their own, build experience, and stay curious. Because data science is not a straight path. It is a landscape full of side paths, discoveries and opportunities.
👉 Curious about where you can get started? Check out our data vacancies and find out which role suits you.
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