Forecasting and Data Analytics Summer Student Employee

Job Title:Forecasting and Data Analytics Summer Student Employee

Location:Palo Alto, CA, Remote/Home Based

Job Summary and Description:

This student position will perform work in one, or potentially both, of the following areas:

1.) Timeseries data analysis to support load and renewables (wind and solar) forecasting research projects.
 

2.) Aid in the setup and running of AI Weather Prediction models and perform analysis of the output to explore their skill relative to each other and traditional NWP for producing weather data pertinent to estimating load and renewable generation for use in planning and operations.

Both these topic areas are core to meeting the challenges of the future electrical grid. The student will work in small teams of EPRI researchers to perform analyses to support real world electrical utility research studies. Students will get applied learning of the importance of meteorological modeling and forecasting for predicting load and renewables for electric system planning and operations tasks. They will improve their knowledge and skills in several of the following areas: timeseries data analytics, AI weather modeling, machine learning analytics and forecasting, and software and programming.

Candidates should have strong timeseries data analytics and visualization skills using a scripting language (Python preferred). Candidates should have familiarity with collecting and analyzing publicly available weather data sets, such as Numerical Weather Prediction (NWP) forecasts and Energy Information Administration (EIA) using APIs and building data pipelines. Nice to have skills include feature selection analyses (e.g., correlation statistics, select k best), familiarity with setting up and running AI weather models, ability to build timeseries machine-learning models (e.g., neural networks, decision trees, regression), and familiarity with performance metrics (RMSE, MBE, etc.).

Ideal candidate will have skills in most of these areas:

• Interest in probabilistic forecasting

• Interest and experience running NWP and one or more AI weather prediction models

• Familiarity with timeseries forecast error metrics such as Root Mean Squared Error (RMSE)

• Familiarity with unit commitment and economic dispatch concepts in power systems

• Experience with creating interactive plots using browser-based packages

Programming Languages:

• Python (primary), R, Julia

Development Libraries/Tools:

• Python scientific computing suites of packages: numpy, xarray, scipy, pandas (or equivalent)

• Visualization frameworks: such as matplotlib, plotly, bokeh, D3.js

• Machine-learning toolkits (1 or more): scikit-learn, keras, tensorflow, pytorch or similar (or equivalent)

• Version control: git (creating issues, performing pull requests, code reviews)

Education:

• Must be enrolled in an accredited university pursuing degree

• Pursuing master's or PhD preferred

The hourly rate range for Student positions are:

• Undergraduate: $16-29 per hour
• Masters: $25-31 per hour
• Ph.D: $27-34 per hour

These ranges are an estimate, and the actual hourly rate may vary based on various factors, including without limitation applicant's education, experience, skills, and abilities, as well as internal equity and alignment with market data. The hourly rate may also be adjusted based on applicant's geographic location.

As an EPRI Student, you will not participate in EPRI’s Benefit Programs which includes health insurance, retirement benefits, vacation, sick leave (except as set required by law) and holiday pay. However, as a Student employee you are eligible for the benefits of Social Security, State Disability Insurance, and Workers’ Compensation Insurance.

For Student positions which require one to relocate to an EPRI office.  Relocation assistance is not provided and the student will be responsible for covering all relocation costs/expenses.

EPRI participates in E-Verify, an online system operated jointly by the Department of Homeland Security and the Social Security Administration (SSA). EPRI uses the system to check the work status of new hires by comparing information from the employee's I-9 form against SSA and Department of Homeland Security databases.

EPRI is an equal opportunity employer. EEO/AA/M/F/VETS/Disabled

Together . . . Shaping the Future of Energy.

www.epri.com