Why data scientist isn’t your first job
You’re not the only one. More and more students are completing a degree in Data Science, AI or Business Analytics and want to start working as a data scientist straight away. But when you check job postings, you’ll see one thing: there are hardly any junior data scientist vacancies - especially not in finance. How come?
We sat down with recruiter Jorn to explore how data science really works in the financial sector - why 'data scientist' is often not the starting point but the destination, and what smarter first steps you can take to kick off your career in data.
Data science in finance: why it works differently
Many graduates think that working as a data scientist means jumping straight into 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 they’re not allowed to do just anything with it. Regulators such as De Nederlandsche 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,” Jorn explains. “And that’s not allowed. Everything a bank does must be explainable and verifiable. You can’t just run an algorithm and say: ‘just trust us’.” That’s why, in finance, a well-aligned collaboration is needed between data specialists like engineers, analysts and stewards to deploy AI and data science effectively and in a controllable way. Think of:
- 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 analyse trends and build dashboards that give managers and regulators quick insights.
Only once all these components are properly 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 clever models, but above all: being able to explain how the model works, why it’s valid, and what the impact is.
Curious what it’s really like to start your first job as a data engineer at a bank? 👀 Check out Andrea’s story – she works as a data engineer at ABN AMRO and shares what her workday looks like!
So why don’t you see any junior data scientist vacancies?
A data scientist isn’t someone with one specific task, but someone who sees the bigger picture: from data collection and analysis to modelling and business impact. And that’s exactly why you hardly ever see junior data scientist roles. “Many organisations don’t yet know how they want to use data science,” says Jorn. “There’s still a lot of pioneering going on. And to pioneer, you need experience.”
What’s more, the role of the data scientist is still developing. At one company, you’ll focus mainly on process optimisation, at another on document recognition or fraud detection. Sometimes it’s called ‘data scientist’, sometimes ‘process mining analyst’, and sometimes something entirely different. The result? Many companies are looking for experienced people who can help make these technologies workable in the first place. And that makes it difficult for graduates.
So how do you get started, then?
Do you eventually want to work as a data scientist? Then it helps to broaden your perspective. It’s not about that one specific job title, but about gaining hands-on experience with data. Think of roles like:
- Data analyst (in finance) – you learn how data influences business decisions.
- Data engineer – you develop the infrastructure that models run on.
- BI specialist – you translate data into dashboards and insights.
But also: business analyst or reporting analyst can be good stepping stones – especially if you’re strong in communication or connecting people and data. “Not everyone needs to do hardcore programming,” says Jorn. “Maybe you’re great at presenting, persuading, explaining. Then you can be the bridge between tech and business – and that’s just as important.”
Whatever role you choose, make sure your CV shows what you bring to the table. 💪 Check out our 10 tips for a strong CV here.
The future of data science is... unpredictable and full of promise
Let’s be honest: no one knows exactly where the field is heading. What’s now called ‘data scientist’ might have a different name in five years. Developments are moving at lightning speed. But one thing, according to Jorn, is certain: “You can be as technically skilled as you like – if you can’t explain it, no one benefits.”
That’s exactly why soft skills are so important. Knowing what you’re doing is step one. Explaining why it matters, and bringing people along in your story – that’s where you make the difference.
Conclusion: build your career from practical experience
Want to work in data science? Then start smart. Choose a role where you gain experience with data processing, get to know the financial sector, and develop yourself in a direction that suits you. There’s no standard path – and that’s exactly your strength. Start with a role where your skills can shine, build experience, and stay curious. Because data science isn’t a straight road. It’s a landscape full of side paths, discoveries and opportunities.
👉 Curious where you can start? Check out our data vacancies and discover which role suits you.
About Solid Professionals
At Solid Professionals, we guide recent graduates in their careers within Finance, Risk, Data and IT. Personal leadership is central to everything we do. Growth starts with knowing yourself and having the space to follow your own path. We help you make choices that suit you, take ownership, and create your own impact. Looking for an exciting graduate role? Check out our vacancies and discover how you can start your career in data!
