Forecast Model Analyst (Hybrid)

California ISO•Folsom, CA

12d•$45 - $63•Hybrid

About The Position

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.

Requirements

A Bachelor's degree (BA, BS) or equivalent education, training or experience in Engineering, Mathematics, Statistics, Computer Science, Meteorology, Economics, or a closely related field.
Two (2) or more years related experience.
Experience in short- or long-term forecasting (Load, Wind and/or Solar generation, Uncertainty) using artificial neural network (ANN), Support Vector Machine (SVM), and/or regression load forecasting techniques.
Open-source project experiences that demonstrate programming, mathematical, and machine learning abilities and interest.
Strong verbal and written communication and documentation skills required, with a demonstrated attention to detail.
Ability to use deductive reasoning and analytical thinking with sound judgment and decision-making skills.
Strong interpersonal and conflict resolution skills are also essential.
Must be self-starting and willing and able to work independently in a dynamic corporate organization under pressure of tight deadlines and aggressive expectations.
Problem solving skills with the ability to influence others without direct authority.
Must be able to work effectively in a team environment as facilitator and team member.
Must be proficient with Microsoft Office Suite.

Nice To Haves

Master’s Degree preferred.
Research experience in Machine Learning, Artificial Intelligence, and Neural Networks (e.g. links to open-source work or link to novel learning algorithms) preferred.
Knowledge and experience in electricity market designs and power system operations desired.
Experience in forecast model technique, tuning, and demonstrated model improvements desired.
Familiarity with Numerical Weather Prediction Models and aptitude for working with the statistical analysis of data and gridded atmospheric data sets.
Familiarity with ITRON forecast model is preferred.
Desired programming languages – SQL, python, R, VBA, etc.

Responsibilities

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.