(The position closes March 26th)
This is an exciting and challenging quantitative, applied economics position in the public sector. Using high resolution, fundamental-based models (both industry standard and in-house applications) and a Monte Carlo simulation, we evaluate how resource mixes and energy markets may evolve over time, and what that may mean for key BPA strategic interests.
Energy economics is endlessly engaging (we focus primarily on micro-level, electricity market dynamics) and you will never run out of new things to learn in this field. You’ll be forecasting grid operations and electricity prices for one of the most challenging regions in the US, given our dependence on hydro generation that can vary substantially from year to year, aggressive state policies to reduce and eventually eliminate carbon emissions in the electric sector, and rapidly evolving energy market designs.
We expect training to take 6 months to 1 year.
The position does have ‘data scientist’ in the title, but we are more interested in the following abilities:
- efficiently and programmatically handle large sets of data in R and SQL (most sets are ~1GB, some sets can be as large as 100 GB, full databases are in the ~3TB neighborhood)
- effectively visualize data
- maintain and create relatively straightforward and explainable models of risk around a variety of factors (variability of wind generation, natural gas prices, and transmission outages)
- provide explanations through clear oral and written communication
…rather than the ability to apply the latest AI / machine learning algorithms.
Please feel free to contact me directly if you have any questions about the position, working at BPA, or the federal hiring process (ewgraessley@bpa.gov)