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We are seeking a Senior Quantitative Risk Manager to develop, enhance, and govern quantitative models used to value, risk assess, and explain exposures across natural gas, LNG, power, and related structured/optional physical and financial transactions in a commodity trading business. The role will partner closely with trading, structuring, origination, middle office, risk, technology, and finance to deliver decision-quality analytics, robust model governance, and scalable reporting.

This role is designed for a candidate who combines cross-commodity quantitative rigor in their quantitative risk leadership with practical energy trading valuation and risk-control orientation.

1) Quantitative Modeling, Valuation, and Analytics

• Develop and maintain quantitative models for valuation, exposure measurement, and risk assessment across physical and financial natural gas, LNG, and power portfolios.
• Build and enhance models for optional and structured transactions, including storage, transport, tolling, heat-rate optionality, basis/spread structures, swing optionality, and other asset-backed or logistics-driven exposures.
• Support mark-to-market, fair value, forward curve construction, volatility surfaces, scenario analysis, and P&L attribution for complex positions and portfolios.
• Design and improve analytical frameworks for VaR, Expected Shortfall, stress testing, backtesting, component risk, sensitivity analysis, and scenario analysis.

2) Trading and Commercial Support

• Partner directly with traders, originators, and structurers to evaluate transactions, challenge assumptions, explain model outputs, and support hedging and optimization decisions.
• Translate market views, deal structures, and operational realities into actionable analytics that support commercial decisions across gas, LNG, and power.
• Provide analysis of risk drivers, spread movements, optionality value, and changes in valuation or risk metrics to risk committees and senior leadership.

3) Risk Framework, Controls, and Governance

• Strengthen the quantitative underpinnings of the firm's market risk framework, including model documentation, assumptions governance, testing standards, and auditability.
• Lead or support model review, model validation readiness, model governance, and remediation of model limitations and control gaps.
• Ensure analytics and reporting align with board-approved risk tolerances, internal policies, and evolving control requirements.

4) Systems, Data, and Automation

• Build or enhance scalable analytics in Python and related tools to automate recurring calculations, improve transparency, and reduce manual risk processes.
• Work with ETRM/CTRM systems and market data infrastructure to ensure robust integration of curves, positions, valuation logic, and risk outputs. Experience with systems such as Endur, Allegro, ZEMA, or comparable platforms is valuable.
• Create reports, dashboards, and visualizations that communicate complex quantitative results clearly to both technical and non-technical stakeholders.

• Advanced Python skills for quantitative analytics, risk engines, data pipelines, and automated reporting; familiarity with pandas, NumPy, SciPy, and production-quality coding practices is expected.
• Additional programming capability in one or more of SQL, C#, C++, VBA, or similar languages.
• Strong understanding of probability, statistics, stochastic modeling, option pricing, numerical methods, Monte Carlo simulation, and time-series analysis.
• Experience with data visualization and reporting tools and the ability to present quantitative insights clearly to senior stakeholders.
• Practical use of AI-enabled tools to accelerate coding, research, workflow automation, data exploration, or insight generation, with appropriate controls for model risk, reproducibility, and governance.
• Familiarity with Git/GitHub/GitLab, software lifecycle controls, and documentation standards is highly desirable.

• Bachelor's degree from an accredited University required in a quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, Econometrics, Finance, Applied Economics or related.
• Master's degree or PhD preferred in a quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, Econometrics, Finance, Applied Economics or related.

• Experience in quantitative risk, quantitative analytics, structuring, valuation, or model development in a commodity trading, energy trading, merchant energy, utility trading, hedge fund, or investment banking environment.
• Demonstrated hands-on experience modeling, valuing, and risk assessing instruments and portfolios in natural gas, LNG, and power.
• Strong understanding of both physical and financial commodity markets, including forwards, swaps, options, structured transactions, and asset-backed exposures.
• Experience with market risk metrics, including VaR/GMaR/EaR/stress/scenario frameworks, and the ability to explain risk in a trading context rather than only from a theoretical perspective.
• Experience in asset-backed trading, including storage, transport, generation, renewables, batteries, or tolling structures in North America gas markets.
• Proven success working cross-functionally with front office, risk, operations, finance, and technology teams.

Preferred Experience / Strong Pluses

• Experience spanning both financial trading and physical energy trading, especially where the role bridged derivatives pricing with logistics, dispatch, storage, or LNG optionality.
• Model validation, model governance, or formal model review experience.
• Exposure to LNG portfolio modeling, shipping/scheduling optionality, or international gas/LNG valuation frameworks.
• Experience supporting power market analytics such as nodal pricing, CRRs/FTRs, heat-rate modeling, dispatch logic, congestion analysis, or ISO/RTO market behavior.
• Ability to mentor junior analysts and influence standards for quantitative methods across the organization.

Core Competencies

• Strong commercial judgment with the ability to connect quantitative outputs to real trading decisions.
• Clear communicator who can explain complex model behavior, assumptions, and limitations to traders, risk managers, finance, and executives.
• High standards for accuracy, transparency, governance, and documentation.
• Comfortable operating in a fast-moving, front-office-adjacent trading environment where priorities evolve and analytics must be both rigorous and timely.