Forecast Model Analyst (Hybrid)

Under the general direction of the Director, participates in the research, development, implementation, monitoring, and continuous improvement of forecasting models supporting short-term load, wind, solar, and uncertainty forecasts. Utilizes advanced statistical methods, machine learning, artificial intelligence, numerical weather prediction, and large-scale data analytics to improve forecast accuracy and operational reliability. Develops methodologies that quantify uncertainty and assess emerging impacts from distributed energy resources (DERs), electric vehicles (EVs), demand response, energy efficiency, building electrification, and other evolving grid trends.What You Will Be Doing:Participates in the research, development, implementation, and maintenance of forecasting analytical tools, and data processes supporting short-term load, wind, solar, weather, and uncertainty forecasting. Validates, integrates, and analyzes large, diverse datasets including weather, load, renewable generation, distributed energy resources, and other operational data sources. Develops automated workflows and enhances existing tools or creates new solutions using programming languages and analytical technologies such as Python, SQL, R, and related technologies. Identifies opportunities to improve forecast processes, data quality, model performance, and operational efficiency.Researches, develops, enhances, and maintains complex energy methodologies and models for load, wind, solar, uncertainty, demand response, energy efficiency, and emerging grid technologies, including distributed energy resources (DERs), electric vehicles, battery storage, distributed generation, and other evolving energy technologies. Applies statistical analysis, machine learning, artificial intelligence, data mining, visualization techniques, and numerical weather prediction data to improve forecast accuracy, reliability, and operational decision support. Evaluates the impacts of changing grid conditions, renewable energy growth, electrification, and extreme weather events on forecasting methodologies and model performance. Develops and maintains probabilistic forecasting methodologies, uncertainty metrics, and model performance monitoring tools. Monitors forecast performance, evaluates model outputs and input data quality, and conducts forecast verification, post-analysis, and model tuning to identify trends, quantify and communicate forecast uncertainty, and recommend enhancements that improve forecast accuracy, reliability, and operational effectiveness.Coordinates and participates in assigned workstreams and participates in forecasting projects and initiatives to implement new models, data integrations, applications, and enhanced functionality into production environments. Collaborates with internal stakeholders to define business and technical requirements, test solutions, resolve issues, and support the successful deployment, monitoring, and operation of forecasting systems and processes.Monitors developments in forecasting, meteorology, data science, machine learning, artificial intelligence, numerical weather prediction, and power system operations. Evaluates emerging methods, technologies, and industry practices for potential application to CAISO forecasting functions and communicates technical findings, recommendations, forecast performance, and model results to management and stakeholders.