What The Role IsClearway Energy seeks a Senior Associate of Risk and Structuring Analytics to develop top-notch quantitative models that drive BESS valuation, structured products pricing, gas generation optimization, and power market analysis, while also quantifying and monitoring the risk exposures those positions create. In this role, you will build and enhance Clearway's internal quantitative modeling platform (CWENQuant), develop advanced BESS dispatch optimization and price forecasting models, and provide independent risk analytics - mark-to-market, value-at-risk, credit, and scenario/stress analysis - that directly inform commercial decisions across trading, origination, development, and asset management.
What You'll Be DoingQuantitative/Economic Modeling- Develop and deploy to production complex optimization models and quantitative analysis for BESS dispatch optimization, renewables, gas generation, and data center infrastructure projects across the US.
- Develop Stochastic or Machine Learning / Deep Learning models to forecast DA, Ancillary, and RT prices based on fundamental variables such as load, gas prices, and wind + solar generation to inform trading.
- Develop and maintain gas dispatch, HRCO, toll, and other structured pricing models; perform mark-to-market analysis; support due diligence, underwriting, and credit analytics.
- Refine vendor models or develop in-house models for capacity expansion with a view on market policy (RPS/GHG reduction), LCOEs, retirements, transmission, reliability, and demand growth.
- Benchmark models against actual operations and market outcomes to improve forecast accuracy and optimization.
- Build and enhance CWENQuant; develop data pipelines with internal and external resources; create automated workflows and market data dashboards using data science and optimization techniques.
- Design and maintain mark-to-market (MtM), Value-at-Risk (VaR), and Profit-at-Risk (PaR) frameworks across BESS, gas, and structured product positions, independent of the deal/trading desks that originate them.
- Run scenario, sensitivity, and stress-testing analyses (price shocks, volatility regimes, basis blowouts, extreme weather/renewable generation outcomes) to quantify tail risk in the portfolio.
- Quantify hedge effectiveness and basis risk for structured transactions, PPAs, and tolling/HRCO arrangements; recommend hedge adjustments.
What You'll Bring- Bachelor's degree in Engineering, Finance, Mathematics, Statistics, Economics, or a related field.
- 3+ years of quantitative or modeling experience within the energy sector, including trading firms, banks, utilities, IPPs, ISOs, or BESS companies.
- Working experience developing ML/DL models using scikit-learn, Keras, or TensorFlow.
- Experience with linear/Mixed Integer programming and associated tools like PuLP, Gurobi, CPLEX, etc.
- Strong quantitative background with deep hands-on experience coding in Python.
- Knowledge of BESS (strongly preferred), renewable, and conventional (gas) assets, HRCO, and toll modeling.
- Strong understanding of power market economics, wholesale market structure, and asset operations in ISO (CAISO, ERCOT, etc.) markets.
- Proven ability to build top-notch optimization models and stochastic or ML/DL forecasting frameworks such as price, load, or renewable generation forecasting.
- Understanding of option pricing, volatility, game theory, opportunity cost, and optimization.
- Ability to formulate and articulate viewpoints (written and verbal) in a clear, persuasive, and succinct manner.
- Ability to handle multiple concurrent efforts and provide high-quality deliverables accurately.
- Ability to work well in a fast-paced, team-oriented, collaborative environment that emphasizes attention to detail, meeting deadlines, and working together to achieve company-wide objectives.
- Strong interpersonal, analytical, and problem-solving skills.
What Would Be Nice- Programming skills required: Python is a must; R, SQL, etc. are a plus.
- Working experience with stochastic models, mean reversion, and jump diffusion preferred.
- Exposure to ETRM systems, MtM, and VaR model validation/governance practices is a plus.
- Master's or PhD is preferred.
No visa sponsorship or any kind of sponsorship for employment authorization is available for this role, now or in the future.#LI-HybridThe pay rate for the successful candidate will depend on geographic location, skills, relevant and demonstrated experience, education, training and certifications, and other factors permitted by law. This role is eligible to earn an annual cash bonus, subject to personal and company performance goals.
Salary Range Across all U.S. Locations
$140,000-$178,000 USD
