Energy Forecasting & Optimization Analyst

CES GridBOOST is an advanced energy management platform that enables grid assets to participate intelligently in electricity markets. As a Data Analyst in the Forecasting and Optimization team, you will sit at the core of the product — owning the quantitative models that determine how GridBOOST bids into day-ahead, intraday, and ancillary service markets. Your work will span two tightly coupled disciplines: building optimization engines that translate market rules and grid constraints into optimal bid strategies and developing machine learning models that forecast electricity prices to inform those strategies. You will work in a cross-functional team alongside software engineers and market operations specialists, with your models running in production and directly influencing real-world market outcomes. There will also be opportunities to learn about and contribute to operational decision-making. Position Responsibilities Design and implement optimization models to optimize energy bids across day-ahead and real-time electricity markets Translate electricity market rules, operational constraints, and grid conditions into mathematical formulations for the GridBOOST bid optimization engine Build and maintain machine learning pipelines for short-term electricity price forecasting, covering day-ahead, intraday, and ancillary service markets Engineer features from weather, demand, generation mix, and historical market signals to improve forecast accuracy Collaborate with software engineers to integrate models into production systems connected to real-time market data feeds Monitor live model performance, conduct systematic backtesting, and iterate on bid strategies and forecast models based on observed outcomes Analyse post-dispatch results and produce clear, actionable insights for trading and grid operations teams Track changes in regulatory frameworks, market mechanisms, and grid infrastructure that may affect model assumptions or bid strategies Document model logic, assumptions, and limitations to support auditability and knowledge transfer Qualifications Bachelor's or Master's degree in Operations Research, Applied Mathematics, Electrical Engineering, Data Science, or equivalent 3+ years of hands-on experience in the electricity domain — energy trading, grid operations, market analysis, or a closely related quantitative role. Demonstrated experience formulating and solving optimization problems for real-world scheduling, dispatch, or resource allocation use cases Proficiency with at least one solver (Gurobi, HiGHS) and an optimization modelling library (Pyomo or similar) Hands-on experience building and deploying ML forecasting models for time-series data, preferably electricity prices or energy load Strong Python skills including pandas, scikit-learn, and at least one deep learning frameworks Experience working with high-frequency time-series market data and external data APIs SQL proficiency for querying and extracting market and operational data Ability to communicate complex quantitative results clearly to non-technical stakeholders Preferred Solid understanding of U.S. ISO/RTO electricity markets, including their clearing mechanisms and pricing rules (Highly Preferred) Hands-on experience building and deploying forecasting and bid optimization algorithms in the electricity markets Prior experience with energy asset operations is preferred; a strong interest in developing operational knowledge is also welcomed. Ability to work with a remote and global teams across multiple time zones