I bought my first share at thirteen, through my father. That is the earliest version of
the thing that has actually driven my career, and it was never really about money — it
was that markets are the cleanest available laboratory for how incentives and evidence
work at scale.
What grew out of it was a habit rather than a hobby: an insistence on knowing whether
something is true before acting on it. That habit ran through the company I built and
sold into, through how I manage property and personal finances, through the boards I
have sat on, and through every commercial role I have held. Retail management is
throught the years has been part of my resume because it was arena direct observe causation clearly.
The commercial decade was real work and I was good at it — a warehouse, then Ahlsell's
largest Norwegian retail location, a second turnaround, three openings, then four years
building a private-label range across working with managent all the in aspects of assoment
stategy and education. In parell introducduced through my own company to moving into marketing,
product, pricing, tenders and supplier negotiation, this then countiue a job role throught my year of studies. It was
also increasingly frustrating. I kept watching decisions worth a great deal of money
get made on instinct, mine included, and I ran out of ways to argue with a spreadsheet.
So I went and learned properly — applied data science at Noroff, full-time, alongside a
full-time job throughout most of my studies. Where i ended up being hired back to Ahlsell,
I was hired as a data scientist in 2023
What I have found since is that my most useful trait is not the technical skill or complex solutions, nor the ability to implement them flawlessly. It is
understanding a business problem being able draw domain life exerpericen, in-turn enriching the solutions I develop with pragmatism. Several of the projects I now own had capable
people on them before me and had stopped; the document-parsing pilot had been open for
about four years also even 10year back. What is missing in many cases is not a better prediction algorithm a further refinement of the model. It was
someone able help interperat stakeholders needs and perhaps even limations within the data, tailor a solution to those
constraints and create a workable implementation, that gains tracktion in turn allow farming data to help improve future iterations.
Rather than seeking perfection from the outset, it is about iterative progress. Which inself comes with struggles,
often spanning outside comfort zones, and requiring resilience; fail fast becomes a guiding principle rather than a mere slogan. I guess that is my guiding principle in life as well or
multiple decades of putting myself in postion conitously driving growth.
Outside work I sit on a housing-association board, manage rental flats in two
countries, endulging enconamy and rebalacing my portfollio, dancing Latin dance, and over-engineer my home
automation. I hold a private pilot licence, which I mention mostly because checklists
changed how I think about deployment.
