Load Forecasting Manager
HybridSan Diego, California, United States
Job Summary
Develop multi-year, hourly load forecasts supporting energy purchasing, hedging, and resource planning while monitoring forecast-to-actual results. Set, monitor, and adjust projected hourly line loss factors, maintain econometric models, and oversee weather-adjusted day-ahead forecasts. Collaborate with internal stakeholders on methodology and analysis, design systems to track historical performance, and conduct macroeconomic research on population and economic growth impacts. Forecast load modifiers including distributed energy resources, electric vehicles, and building codes; estimate prior month revenues and support rate design and cost of service analysis. Coordinate with external entities like the California Energy Commission on utility forecasting assumptions and IEPR filings.
Required Qualifications
- Knowledge and experience with econometric methods, tools, and software
- Mathematical background and strong analytical and problem-solving skills
- Strong analytical and problem-solving skills
- Ability to balance multiple priorities to meet deadlines
- Strong work ethic and comfort taking initiative
- Professional communications skills, in writing and verbally
- Proficiency with Microsoft Office Suite, Word, Excel and PowerPoint
- Ability to communicate and collaborate effectively with a variety of individuals representing diverse cultures, backgrounds, and languages
- Bachelor's degree or higher from an accredited college or university in economics, statistics, data science or related mathematic field
- At least five years' experience in load forecasting or equivalent
- Occasional local travel
- Reliable transportation
- Fully vaccinated for COVID-19
- Ability to file a Statement of Economic Interests (Form 700) on an annual basis
Desired Qualifications
- Familiarity with database languages such as SQL, R, or Python and other related applications as well as analytics/business intelligence platforms such as PowerBI and Tableau
- Experience with the utility industry, working for an electric investor-owned or public utility company, a Community Choice Aggregator, or similar electric sector company
- Master's degree in statistics, econometrics, data science, or closely related field
- Expertise with database languages such as SQL, R, or Python and other related applications, familiarity with machine learning and business intelligence platforms
- Experience and knowledge of statistical modeling techniques such as GLM multiple regression, logistic regression, log-linear regression, time series, stochastic, probabilistic for reliability, and Bass Diffusion modeling techniques
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