Sector
Data recruitment for financial services and insurance
Insurers and banks hold decades of data and a shortage of people who can put it to work inside a regulated environment. Linkrs recruits data engineers, analysts, scientists and AI specialists for financial institutions in Belgium and the Netherlands — from analytics engineer to chief data officer.
The data and AI talent market in financial services
The raw material is not the problem. A typical Belgian or Dutch insurer sits on decades of policy, claims and customer data — locked in legacy administration systems, mainframe extracts and departmental spreadsheets. The constraint is engineering: people who can build modern data platforms while respecting the realities of a regulated institution, where lineage must be provable, access must be auditable, and the source systems cannot simply be switched off. Platform engineers who accept those constraints as part of the craft, rather than an obstacle to it, are the profiles every institution is chasing.
AI in financial services is leaving the pilot phase, and the exit door is guarded. The EU AI Act, alongside long-standing supervisory expectations on model risk from the NBB and DNB, means a model that reaches production must come with governance: documented data quality, explainability appropriate to the use case, monitoring, human oversight. This changes the hiring profile fundamentally. FS institutions need AI and machine-learning engineers who treat model governance as a design requirement — and screening out the candidates who will experience it as bureaucratic friction saves everyone a painful first year.
The competition for this talent is not other insurers; it is technology companies, scale-ups and consultancies offering faster stacks and fewer constraints. Financial institutions cannot win by imitating that pitch, and the smart ones stop trying. What they can offer is real: problems with consequence — pricing, fraud, claims decisions that affect actual households — mature data at scale, stability, and increasingly serious engineering investment. Positioning a role that way to a sceptical engineer is a craft, and it is one of the places where a specialist recruiter earns the fee.
The boundary between data science and actuarial work is blurring, most visibly in pricing and fraud detection. Actuaries adopt machine-learning methods; data scientists move into pricing teams and inherit actuarial review processes. Both movements create hybrid roles that neither a pure-tech recruiter nor a traditional actuarial recruiter reads correctly. Because we work both disciplines in the same market, we can judge whether a role — or a candidate — belongs on the actuarial side of the line, the data side, or genuinely in between.
Data and AI roles we place
Data engineering and architecture
- Data Engineer
- Senior Data Engineer
- Analytics Engineer
- Data Architect
- Data Platform Engineer
Analytics, science and AI
- Data Analyst
- BI Developer
- Data Scientist
- Machine Learning Engineer
- AI Engineer
Governance and leadership
- Data Governance Officer
- Data Quality Analyst
- Data Management Specialist
- Head of Data
- Chief Data Officer
How Linkrs recruits data and AI talent
Data professionals evaluate opportunities the way they evaluate systems: on specifics. Stack, data maturity, team composition, how far the modernisation has actually progressed versus the slide deck. Our approaches carry those specifics because we take the time at intake to understand them — and because the partner who understood them makes the calls. That is also why our candidates show up to first interviews already believing the role is real.
This discipline spans two employment markets, and we work both honestly. Freelance data engineers command strong day rates in Belgium and the Netherlands, and for a platform build-out phase that can be the right construction; for the team that must run and govern the platform afterwards, it usually is not. We advise clients on that split at intake, and we tell candidates plainly which institutions offer engineering environments worth a permanent commitment. Shortlists are short, assessed and candid, and every permanent placement carries a guarantee period.
When an institution is standing up a data capability — a new platform team, an analytics function, an AI engineering group — hiring one profile at a time through contingency fees rarely makes sense. Linkrs Embedded places our sourcing capability inside your team for the duration of the build. At the other end of the scale, head of data and chief data officer appointments run as retained Linkrs Mandate searches, mapped across both countries and handled with the discretion a leadership change requires.
Frequently asked questions
We lose data engineering candidates to tech companies. How do you compete with that?
Not by copying the tech-company pitch — by making yours honestly. Engineers choose FS roles for consequential problems, genuinely large and messy data, stability and increasingly credible platform investment; they reject roles that oversell modernity and undersell constraints. We position your role truthfully from the first call, which filters for candidates who will actually stay, and we will tell you at intake if parts of your proposition need fixing before the search can succeed.
Does the EU AI Act change what profile we should hire?
Yes, materially. Models in production now need documented governance — data quality, explainability, monitoring, human oversight — so the engineer who ships fast but resents review processes becomes a liability rather than an asset. We screen specifically for candidates who have delivered models inside a governed environment, or who demonstrably understand why the guardrails exist. That temperament check is as important as the technical one.
I am a data scientist in tech. Is moving into insurance a step backwards?
For the right person it is a step into ownership. Insurance pricing, fraud detection and claims analytics offer problems where your model directly moves the business, datasets tech companies rarely match in history and depth, and far less competition for internal visibility. The trade-off is real governance around what you ship. We will give you an honest picture of which Belgian and Dutch institutions have engineering cultures worth joining — they are not all equal.
Where does a data role end and an actuarial role begin?
Increasingly, nowhere clean — pricing and fraud teams now mix actuaries using machine learning with data scientists absorbing actuarial review discipline. Practically, it comes down to accountability: roles inside the actuarial function carry regulatory weight and usually expect actuarial education, while data roles answer to engineering and governance standards. We recruit on both sides of that line and can tell you, for a specific vacancy or career move, which framing fits.
Hiring in data & ai?
Tell us about the role. A partner will come back to you with a realistic read of the market — usually within one business day.
Related: Actuarial · Business Analysis & Product