I am currently struggling a bit with my future career path. Because job opportunities in the energy sector are limited in my country, I am trying to identify alternative routes. However, I do not want to stop working on energy markets and the sector itself. For me, it seems somewhat easier to enter the finance industry. Which department within finance would best support this long-term goal? I have been thinking that starting in a treasury department might be helpful, since it is directly related to monetary conditions and financial management. What do you think about this? If there is no direct path into energy markets, how should I build an alternative route that would still move me toward that objective?

Rising oil prices from Middle East conflict spread quickly through the global economy. They have: a) increase dinflation, b) pushed up short term bond yields, c) strengthened the US dollar, d) raised costs for oil importing countries. A new report on this topic has now been published in Classroom, at the very end under “Energy Industry Support”, a special section featuring reports that explain the current status and key trends in the energy sector. The report is written in simple language, includes illustrative graphs, and shares official sources from the Financial Times, Bloomberg, Wall Street Journal, The Economist, Forbes, and Investors Chronicle. It can be freely used in your projects, work, or studies. It may be especially useful for interviews, presentations, networking, and broader professional development. It is strongly recommended to read it and download it for your use. See the attached screenshots.

Recent interview question in Chevron. Happy to have some inputs. Thanks . - Department: Corporate Strategic Planning, interviewing jointly with Chevron New Energies (the division responsible for lower-carbon investments like hydrogen and carbon capture). - Target Role: Senior Quantitative Energy Economist - Interview Stage: Final Round / Executive Panel Presentation. You would likely be standing at a whiteboard in front of 3 to 4 senior directors. - 2 February 2026. - Format: the recruitment coordinator takes your mobile phone, laptop, and smartwatch. There is no AI, no internet, and no Python to run your simulations. You are led into a quiet focus room. On the desk is a printed piece of paper containing "The Carbon vs. Capital Conundrum" prompt, a basic scientific calculator, a notepad, and a pen. You are given exactly 45 minutes to digest the prompt, formulate your economic models from memory, and structure your recommendation.After 45 minutes, you are escorted into the boardroom to face the senior directors. You have nothing but your handwritten notes, a whiteboard, and a marker. Interview Question: " You are presenting to our executive investment committee. We have a strict capital expenditure (CapEx) limit for the upcoming fiscal year and can only fully fund one of two mega-projects. You must recommend which one we choose: Project Alpha (Deepwater Oil & Gas) - Location: Offshore West Africa - Financials: Spectacular projected Internal Rate of Return (IRR) of 20% with a very fast payback period. - Risks & Downsides: The host country is experiencing growing political instability. Furthermore, the project has a massive carbon footprint that will push our corporate emissions well over our stated public reduction targets for the decade. Project Beta (Carbon Capture & Hydrogen Hub) - Location: US Gulf Coast - Financials: The economics are extremely tight. The baseline IRR is only 7%, which barely clears our corporate hurdle rate (minimum acceptable return). - Benefits: It operates in a highly stable geopolitical region, secures massive government tax credits, and practically guarantees we hit our corporate net-zero pledges.

A new online course is now available in the Classroom section: "115. Monte Carlo Simulations in Energy". Because we are building directly on our previous work, please make sure you have completed the prerequisite course, "114. Deterministic Optimisation in Energy," before diving into this one. In the real world, sunlight and electricity demand fluctuate constantly. Relying on a single "perfect" forecast leaves power systems highly vulnerable to unexpected costs or failures. This course teaches you how to handle that uncertainty using a Monte Carlo Simulation. We will take our deterministic model and run it 1,000 times, applying random, normally distributed variations to both the electricity demand and the solar output. By analyzing the 1,000 optimal costs through statistics and risk metrics like VaR (Value at Risk) and CVaR, we observe how to observe how sensitive are the economics to uncertainty. Mastering this technique is crucial in the energy sector because it allows to mathematically quantify risk and predict financial stability of energy systems.

I was in a project collaboration (Enel, EDF, JP Morgan, Universities etc) on algorithmic trading, and the use of Machine Learning for electricity price prediction. I am sharing one slide. Why are trading desks using Random Forest so much ?