Oslo, Oslo, Norway
I build fundamental supply/demand models for crude oil, refined products, natural gas & LNG, and European power, turning them into actionable research for institutional clients. At DNB Carnegie, I'm part of a commodity research franchise consistently top-ranked in Norway (Prospera survey). My work spans the full pipeline: sourcing and structuring data, building in-house models, producing client-facing research, and engaging with investors across Europe and globally. I started my career at Norges Bank, working on macroeconomic modeling and policy analysis, and graduated top of my class at BI Norwegian Business School (rank 1 of 600). I later completed PhD coursework in Finance at UCLA Anderson before deciding to return to industry. My background combines fundamental knowledge of energy markets with quantitative training in finance and econometrics, and hands-on experience building data infrastructure in Python, R, and MATLAB.
Our core focus is on the oil, gas, and power markets. We leverage large amounts of data and industry expertise to model the fundamental balances for crude and oil products, natural gas/LNG, and European power.
I worked on structuring and analyzing data on commodity and currency forecasts and automation of data gathering for a project. I also wrote Python code for a data analytics and machine learning class.
A position tailored to get talented students to top-ranked doctoral programs in the US. I assisted on several research projects as a Junior Researcher while preparing my Ph.D. application package.
The unit develops models and analytical tools to enhance the basis for monetary policy decision-making and macro-prudential policy guidance. The unit has a special responsibility for developing and estimating Norges Bank’s core macroeconomic model, NEMO, and the system for averaging short-term forecasting models (SAM). The unit develops and deploys models for macro-prudential stress testing. In addition, the modeling unit develops macro-models based on heterogeneous agents which are estimated/calibrated based on microdata.
Worked for several Professors. Some of my tasks were (i) assisting with replication of papers, (ii) automated processes for data gathering (mainly web-scraping, e.g., writing programs that download images or text to extract data), (iii) assisted with helping students with programming related issues, (iv) wrote a text-matching algorithm for a research project, (v) structuring textual data.