What I bring
The shortest version is this: I bring a strong analytical base from NHH, an A-graded empirical thesis, a long record of discipline from senior football, and a serious interest in AI backed by practical and responsible use.
The clearest fit is in analytical work across capital markets, investing, equity research, and other research-heavy environments, especially where modern tools can improve efficiency without lowering standards.
The strongest six points
Click through the areas that matter most to you.
Good underlying finance and methods training
The academic case is solid before anything else gets added on top. That matters because football and AI are only persuasive if the finance base is already credible.
Evidence
- Bachelor of Science in Economics and Business Administration, NHH.
- Master in Economics and Business Administration, with Financial Economics as the main profile, NHH.
- Master's GPA: 4.45 / 5.00.
- Coursework includes Investments, Corporate Finance, Financial Econometrics, Trading and Numerical Methods in Finance using Python.
Why it matters
I should not need an unusually long runway to understand financial statements, company analysis, market material, or empirical work. The finance training is strong, and it sits on top of a broader business and economics base.
Comfortable taking a difficult question and making it workable
The best academic evidence is not the grade alone. It is the type of work behind the grade.
Evidence
- Thesis title: Do European corporations benefit from divesting to private equity acquirers?
- Thesis grade: A.
- The work used large-scale transaction data, firm-level financial data, data matching, cleaning, and applied econometric analysis.
Why it matters
A lot of entry-level finance work boils down to structuring messy information, checking it properly, and forming a view. This is the strongest direct evidence that I am comfortable doing exactly that.
Long-horizon discipline is already tested
This is where the football background matters most. Not as branding, but as evidence.
Evidence
- Football commitment ran from 2012 to 2025.
- Six years in Viking's academy from age 12 to 18.
- Roughly 18 hours per week of structured training during full-time MSc studies, plus travel, recovery, and preparation.
- A major hip surgery in 2022 meant no competitive matches again until 2024.
- The return ended with promotion in 2025.
Why it matters
I am used to demanding routines, delayed rewards, and staying consistent when the work is repetitive or difficult. That is valuable earlier than many people think.
Not surprised by feedback, standards, or selection pressure
This point is stronger when it stays concrete. The football path runs through several clubs, several levels, and enough volume to say something real.
Evidence
- The club path runs through Viking, Start, Åsane, Sotra, and Vidar.
- National league winner and NM finalist with Viking G16.
- Åsane U23 Player of the Year in 2021.
- 32 matches in Norway's 2nd Division with Sotra, followed by promotion in 2025.
Why it matters
High-demand environments are not abstract to me. Selection, accountability, and performance review are already familiar parts of how I have worked for years.
Range without losing the core profile
The profile benefits from range, as long as the center of gravity stays clear.
Evidence
- The profile is finance-led, but built on full economics and business administration degrees at NHH.
- UC Berkeley exchange adds international context and a wider academic frame.
- Open to roles across Norway, the Nordics, and selected European markets.
Why it matters
I do not look locked into one narrow mold. The broader academic base makes it easier to understand financial questions in a wider business context and to fit into teams where the work spans research, execution, judgment, and rapid learning.
Can use modern AI productively without being careless
This matters because many teams want the upside from AI, but not the sloppiness that often comes with it. I think the value is in using modern tools seriously, with curiosity and clear limits.
Evidence
- I spend a lot of time following new models, tools, workflows, and updates from the major AI companies.
- I use LLMs, agent-based tools, automation, and code to speed up research, structure information, and build cleaner workflows.
- Time at Berkeley strengthened that interest by putting me around people who were closer to AI engineering and more technical AI work.
- I have used AI throughout finance and economics work to gather, organize, compare, and pressure-test information more efficiently.
- When information is sensitive, I think carefully about what should stay local, what can be abstracted, and where code or local tools are better than pushing data into external systems.
Why it matters
Teams do not just need enthusiasm about AI. They need people who can use it to work faster, build safer processes, and still respect confidentiality, judgment, and accountability.