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 about the cleanest laboratory I know of for how incentives and
evidence play out at scale.
What grew out of it was a habit rather than a hobby: wanting to know whether something is
actually true before acting on it. That habit has run through the company I built and
eventually sold, through how I manage property and personal finances, through the boards
I have sat on, and through most of the commercial roles I have held. Retail management
stayed part of my résumé over the years mainly because it is an arena where you can watch
cause and effect play out directly, without much room to hide behind a narrative.
The commercial decade was real work, and for the most part it went well: a warehouse,
then Ahlsell's largest Norwegian retail location, a second turnaround, three store
openings, and then four years building out a private-label range — assortment strategy,
supplier education, and, in parallel through my own company, a move into marketing,
product, pricing, tenders and supplier negotiation, some of which carried on as a role
alongside my studies. It was also increasingly frustrating. I kept watching decisions
worth a lot of money get made on instinct — mine included — and I ran out of patience for
arguing with a spreadsheet that had no evidence behind it.
So I went and learned it properly — applied data science at Noroff, full-time, alongside
a full-time job for most of my studies. I ended up being hired back to Ahlsell, as a data
scientist, in 2023.
What I have found since is that my most useful trait probably isn't technical skill on
its own — that matters, but it's rarely the actual bottleneck. It's more often being able
to draw on domain experience to understand a business problem well enough that whatever I
build ends up pragmatic rather than just technically correct. Several of the projects I
now own had capable people working on them before me, and progress had stalled anyway —
one had been sitting open for years before I got to it. In cases like
that, what's missing is rarely a better model. More often it's someone willing to sit with
the stakeholders, work out what they actually need and where the data falls short, and
shape something workable inside those limits — something that gets enough traction to
keep improving from there. I lean toward iterative progress over waiting for a clean
answer, which is uncomfortable more often than not and takes a fair bit of deliberate
resilience; "fail fast" is less a slogan to me than a fairly accurate description of how
it usually goes. I think that has quietly become something close to a guiding principle
for me more generally — put yourself in a position to keep learning, and accept that most
of the outcome sits in a grey area rather than in a clean win or a clean failure.
Outside work I sit on a housing-association board, manage rental flats in two countries,
keep an eye on markets and rebalance my own portfolio, dance Cuban salsa, and probably
over-engineer my home automation. I hold a private pilot licence, which I mention mostly
because checklists changed how I think about deployment.
