Quantitative Risk Analyst Jobs (NOW HIRING)

ABOUT THE POSITION

The Quantitative Risk Analyst will play a key role in advancing the firm's modeling and risk analytics capabilities to support critical commercial and operational decision-making across our power and natural gas portfolio. This position is embedded within the Commercial Operations and Risk Management teams and is responsible for delivering actionable quantitative insights that inform fuel strategy, dispatch optimization, margin and basis exposure, capacity planning, liquidity management, and new project evaluation. This role requires strong quantitative acumen and the ability to build practical, decision-ready models in environments where data may be incomplete or evolving.

The Quantitative Risk Analyst will apply both deterministic and probabilistic methodologies across fuel supply analysis, dispatch optimization, portfolio risk measurement, and broader commercial analytics. Working cross-functionally with commercial, operations, and finance teams, this individual will translate complex analytical outputs into clear, decision-focused insights for leadership. The position will be instrumental in maturing the organization’s analytical framework—supporting both near-term operational performance and long-term capital planning initiatives.

KEY RESPONSIBILITIES Modeling & Risk Analytics

• Analyze price volatility, correlation structures, operational uncertainty, and margin/earnings drivers across fuels, basis, and power markets

• Conduct margin, earnings, and cash-flow at-risk analysis of thermal, renewable, and intermittent generation

• Model plant-level earnings and variable margin at risk using deterministic and probabilistic methods

• Support transaction-level risk measurement, including exposure, sensitivities, and VaR-type metrics appropriate for the firm's portfolio

• Estimate potential future exposure (PFE) for collateral and commodity transactions using available inputs and practical simulations • Evaluate fuel supply, storage, transport options, and dual-fuel dispatch constraints • Support structured contract and PPA valuation • Analyze project economics for new development and acquisitions

• Run deterministic cases, sensitivities, scenario analysis across fuels, basis, weather, and dispatch drivers, and historical/hypothetical/asset-specific stress tests

• Execute probabilistic or Monte Carlo simulations, as appropriate

• Document methods, implement model and data audit controls, and ensure models are reproducible

Market & Portfolio Risk Measurement

• Measure and explain exposure across hub and nodal power markets

• Track and analyze natural gas, transportation, storage, and basis exposures

• Evaluate congestion, DA/RT dynamics, and operating uncertainty impacts on exposure

• Monitor capacity market/auctions and ancillary services exposure

• Provide clear P&L, liquidity, and operating exposure attribution to management

Forward Curves & Marking

• Assist with forward curve development and maintenance for power and natural gas, and adjacent commodities and markets

• Develop and maintain basis curves

• Build nodal and hourly shape curves and seasonal adjustments, where appropriate

• Model seasonal and weather-driven price sensitivities, and other fundamental factors

• Support curve validation and publishing for internal use

Professional

o 2–6 years in energy modeling, risk analytics, or quantitative valuation

o

Understanding of power and natural gas markets (heat rates, dispatch, basis/transport)

o Ability to apply and explain stochastic or probabilistic methods

o Demonstrated ability to work in a hands-on role in a lean environment, requiring independent problem-solving and comfort operating with incomplete data

o Experience in PJM, ERCOT, ISO-NE, NYISO, MISO, SPP, or CAISO markets

o Experience with market data from ISOs, pipeline postings, and market data vendors

o Familiarity with valuation tools (MATLAB, R, @Risk, CQuant, or similar)

o Exposure to PPA valuation, tolling, hedging, or structured transactions

o Understanding of BESS concepts and modelling