Student employee

Salary Competitive

We are looking for a student assistant who wants to work at the intersection of insurance, data, and technology.

A key responsibility will be to validate and structure policy data so that our systems can correctly understand and utilize the information. You will collaborate with our Tech department to improve how our AI models read and interpret insurance policies received from insurance companies.

This means that you will not only be checking data, you will help ensure the quality of the data foundation that our automation and advisory services rely on.

In addition, you will contribute to:

Supporting KPI reporting and underlying data preparation
Billing-related processes
Collecting and structuring insurance policies from partner insurance companies

The role requires that you work in a structured and detail-oriented manner, while also understanding how data fits into a broader business context.

You will gain insight into how a digital brokerage business is built - both technologically and operationally.

You will report to the company’s Head of Administration and for this role it's a prerequisite that you speak danish.

For more information or questions please contact us at ch@norlix.com or phone number 30131080

Perks and benefits

This job comes with several perks and benefits

Near public transit

Free office snacks

Paid holiday

Free lunch

Skill development

Pension plan

See all 7 benefits

Working at
Norlix ApS

See company profile

At Norlix, we are building a digital insurance and pension brokerage from the ground up. The industry is still heavily characterized by manual workflows, legacy systems, and advisory services that are typically activated only during scheduled reviews or when the client reaches out themselves. This creates friction and means that coverage and risk do not always evolve in step with the company. We are designing a different model. We combine integrations, structured data, and continuous analysis to make advisory services proactive rather than reactive. In practice, we focus on: - Automated and continuous data collection - Ongoing assessment of risk and coverage - Systematic monitoring of the market - Data-driven optimization instead of periodic reviews The ambition is simple: To make advisory services more precise, more transparent, and less dependent on manual processes. We are still in the build phase. This means the systems and processes we are developing now must be able to scale.