Clearway Energy
Senior Associate, Risk and Structuring Analytics
$140K–$178K
Houston, Texas, United States +5 · Senior · Full-time · Posted Oct 5, 2026
- Location
- Houston, Texas, United States
Denver, Colorado, United States
Princeton, New Jersey, United States
San Diego, California, United States
San Francisco, 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 storage, generation, structured products, and power markets to inform commercial decisions.
What you'll do
- Build and deploy optimization, forecasting, pricing, and capacity expansion models; enhance the CWENQuant platform and related data workflows.
- Develop and maintain structured pricing and mark-to-market models, and support due diligence, underwriting, and credit analytics.
- Measure portfolio exposures using VaR, PaR, scenario and stress testing, and hedge-effectiveness 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 within the energy sector.
- Working experience developing ML/DL models using scikit-learn, Keras, or TensorFlow; experience with linear/Mixed Integer programming and tools such as PuLP, Gurobi, or CPLEX.
- Strong quantitative background and hands-on Python coding experience; knowledge of power market economics, wholesale market structure, and ISO asset operations.
Nice to have
- BESS knowledge; R or SQL; stochastic models, mean reversion, and jump diffusion; ETRM systems and MtM/VaR model validation or 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 stochastic or machine learning/deep learning models to forecast day-ahead, ancillary, and real-time power prices.
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