I pulled together some notes from recent discussions on employment conditions across countries, especially for energy/utility roles. Sharing here in case it helps anyone comparing markets. I have attached the Excel file. The file is broader employment/job-market comparison by country, and only one part of it is energy-related via the “Utility Sector Security” column.Useful if you’re comparing job stability vs compensation in the broader energy sector. Big picture: - USA = highest salaries, but weaker general job security and social safety net - UK / Australia = strong employment protections and very stable utility-sector roles - France = strongest labor protections and very hard to dismiss employees - Switzerland = very high salaries with a strong financial safety net

Just wanted to share that from my recent talks in board meetings and with executives globally, the consensus is that companies are indeed laying off, and these numbers are accurate that I share below and maybe you have seen also around . You can verify them yourself. While they largely represent cumulative cuts and aggressive restructuring from 2023 through early 2026, the direct job eliminations are very real. I checked some of the biggest tech and legacy players: - Amazon: 30,000 (Cumulative cuts across corporate and managerial roles) - Intel: 25,000 (Aggressive restructuring to pivot toward the AI market) - Microsoft: 15,000 (Accumulated over several rounds of tech-sector corrections) - Dell: 12,000 (Massive sales team reorganization) - Salesforce: 4,000 (Role eliminations to reallocate funds toward AI development) - GM (General Motors): 3,300 (Cuts across autonomous vehicle divisions, software, and factory shifts) In the energy industry, we are going through the exact same ruthless transition, and the ones losing their jobs are the ones who have completely failed to adapt to new technologies and who rely on an old mindset. The industry is rapidly pivoting into professionals who understand / explain/ can storytell machine learning models, AI integration, optimization etc. The professionals who are aggressively upskilling and learning how to integrate data science into traditional energy workflows are the ones keeping their seats . If you think that energy is "safe" and all I have to do is "just go to meetings, and do coffee breaks" this is not the truth! "Oh I studied 5 years. I am now going to have a relaxed professional life". No! Constantly improve your skills. Never settle. Those who think 'energy is just writing reports and go to meetings" may get anytime a phone call "My friend, I love you. But I am sorry to say, you are among those who are going to be laid off. I am truly sorry. You know how much I care but I am sorry. It is a company policy". And that's it.

The course (available in Classroom) teaches how to develop a profit-maximizing arbitrage strategy for energy storage using mathematical optimization (Linear and Mixed-Integer programming) in Python. Full Python code available to download and fully explained in the video (1 hour and 15 minutes). No prerequisites (beginner-friendly). Energy storage can make money through various ways, one of which is energy storage trading (arbitrage). See the attached figure summarising the energy storage arbitrage strategy. The algorithms and strategies taught in this course are the industry standard for the following roles: - Quantitative Analysts (Quants) in energy firms - Energy Traders - Data Scientists (Energy) - Asset Managers Where is this code used? This specific type of optimization (Arbitrage & Dispatch) is used in firms across the energy and financial sectors: - Hedge Funds & Prop Trading - Investment Banks - Commodity Trading Houses - Energy Majors & Tech Energy Storage : - Utilities (e.g., Duke Energy, NextEra, Enel) own batteries (energy storage) to help stabilise the electricity grid. - Independent Power Producers (IPPs) (e.g., Vistra, AES, Neoen) are companies that build and own power plants (solar, wind, batteries) specifically to sell electricity for profit. They are very active users of arbitrage strategies. - Investment Funds (e.g., Gresham House, Gore Street Capital) are specialized funds that buy batteries as financial assets, similar to how a real estate fund buys apartment buildings to collect rent. - Hedge funds (like Citadel, D.E. Shaw, or Millennium) thrive on volatility. In energy markets, prices can jump from $20 to $2,000 in minutes.

No need to know quantitative finance to answer this question. I am attaching a presentation of 3 slides . A very common interview question in Energy (not only energy finance) is about the electricity price and its characteristics, one of which is 'mean reversion' . Such an interview question is when you apply for roles like the following. I also show company names. - Energy Quantitative Analyst (Vitol, BP, Shell, Citadel, Millennium, RWE, Statkraft) - Power / Gas Trader (Mercuria, TotalEnergies, EDF, Goldman Sachs, JP Morgan, Morgan Stanley) - Energy Structurer / Originator (Engie, Vattenfall, Macquarie, D.E. Shaw, DRW, ExxonMobil) - Market Risk Analyst (Uniper, Centrica, J.P. Morgan, Barclays) - Data Scientist - Commodities (Freepoint Commodities, Castleton Commodities, Koch Industries) If you look at a graph for the electricity price, for every hour of the year you will see them fluctuating, spiking high during sudden shortages or dropping low during excess electricity supply. However, the price always snaps back to a central equilibrium level (mean). So it 'reverts' back to the mean. So the price fluctuates wildly but it is always being pulled back toward its seasonal trend line. This trend line (mean) is not a flat / horizontal line; but it also fluctuates , but slowly, throughout the year due to seasonal demand . So in the interview question they show a plot of the electricity price. Just like the one attached. They ask you to explain where mean reversion is and why it happens. Click below to download the presentation . By the way, there are many mathematical models to describe this behavior. We will see them all in Python. The most common is called "Ornstein-Uhlenbeck" model. It is used by all quantitative analysts. Another one , more realistic version, is the Mean-Reverting Jump Diffusion model. You can just know the name of these models. Nothing more. We will see the code and theory later.

A new report on energy trends has been published and can be found by clicking on 'Classroom' and navigating to Section 6.2 (See the attached screenshot). You can use this report and the visualisations it includes, in your own projects, work, or studies, without limits. This report is about Small Nuclear Reactors and current trends by February 2026. Big technology companies like Amazon and Google are racing to find reliable electricity to meet the massive energy demands of new AI data centers. Their primary long-term solution is investing in Small Modular Reactors (SMRs) which are smaller nuclear plants that provide steady "zero-emission" electricity. However, because SMRs take about 8 years to build, these companies are also restarting and upgrading existing nuclear plants to bridge the gap. The report includes lots of diagrams and flowcharts that provide context, and also a list of relevant sources that were used to complete this report. These sources are from the Financial Times, Wall Street Journal, the Economist and Investors Chronicle (all sources are available inside the report). Your subscription in this Skool community gives you access to paywalled energy-economics articles from these publications (Financial Times etc) indirectly through these reports. I have also included some explanations and additional text that explains some details. The text is written in beginner-friendly, easy-to-understand language. Reading these reports is helpful for interviews, panel discussions , presentations, networking, and public speaking. Strongly recommended.