Clearway Energy
Senior Associate, Risk and Structuring Analytics
$140K–$178K
San Francisco, California, United States +5 · Senior · Full-time · Posted Oct 6, 2026
- Location
- San Francisco, California, United States
Denver, Colorado, United States
Houston, Texas, United States
Princeton, New Jersey, United States
San Diego, California, United States
Scottsdale, Arizona, United States - Experience
- 3+ years of quantitative or modeling experience within the energy sector.
- Education
- Bachelor’s degree in Engineering, Finance, Mathematics, Statistics, Economics, or a related field.
- Visa sponsorship
- No visa sponsorship or any kind of sponsorship for employment authorization is available for this role, now or in the future.
About the job
Develops quantitative models and independent risk analytics for energy asset valuation, pricing, dispatch optimization, forecasting, and portfolio risk.
What you'll do
- Build and deploy optimization, pricing, and forecasting models for battery storage, renewables, gas generation, and related projects.
- Develop and maintain CWENQuant, data pipelines, automated workflows, and market data dashboards.
- Measure and monitor portfolio exposures through mark-to-market, VaR, scenario and stress testing, and hedge analysis.
What they ask for
- Bachelor’s degree in Engineering, Finance, Mathematics, Statistics, Economics, or a related field.
- 3+ years of quantitative or modeling experience in the energy sector.
- Working experience developing ML/DL models using scikit-learn, Keras, or TensorFlow; experience with linear/Mixed Integer programming tools.
- Strong quantitative background and hands-on Python coding experience.
Nice to have
- Knowledge of BESS, renewable and gas assets, HRCO, and toll modeling; stochastic modeling experience is preferred.
- R, SQL, ETRM systems, MtM and VaR model validation/governance; Master’s or PhD.
Pay
$140,000–$178,000 USD (base salary)
Tools
Python, scikit-learn, Keras, TensorFlow, PuLP, Gurobi, CPLEX, R
AI focus
Develop machine learning/deep learning forecasting models for day-ahead, ancillary, and real-time power prices, and apply optimization and data science to energy dispatch and market analysis.
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