Senior Quantitative Risk Analyst

NextEra Energy•Juno Beach, FL

About The Position

The Senior Quantitative Risk Analyst advances quantitative risk models and methodologies, developing complex models to measure market, credit, and liquidity risks and contributing to operational-level strategic decisions. The role leads model innovation and refinement within NextEra Energy’s Quantitative Risk Management team, which aims to strengthen proactive risk assessment and inform decision-making in energy and commodity markets. The analyst constructs, validates, and fine-tunes risk models, translates algorithms and code from other languages into Python, and develops valuation models for exotic products such as weather derivatives and structured products. The role also conducts scenario analysis, stress testing, and back-testing; supports closing processes and risk-management discussions; and contributes to trading-system transformation, multi-platform integration, analytics, and automation projects. The analyst collaborates across teams, follows SOX control processes, promotes ethical and proactive risk practices, and mentors junior colleagues.

Requirements

Bachelor’s degree.
At least 4 years of experience, or at least 3 years of experience with trading experience.
Expert-level Python programming skills and experience with financial modeling libraries.
Strong understanding of risk methodologies, including VaR, stress testing, and scenario analysis.
Proficient AI skills for process automation using AI agents, with knowledge of MCP/LLM.

Nice To Haves

Advanced degree in Quantitative Finance, Operations Research, Financial Engineering, Applied Mathematics, Economics, or a related field.
Ph.D. in Quantitative Finance, Operations Research, Financial Engineering, Applied Mathematics, Economics, or a related field.
Experience in quantitative risk management, preferably in energy or commodity markets.
Experience valuing exotic derivatives and modeling structured products.
Knowledge of energy markets and power market fundamentals.
Experience with trading-system transformation and multi-system integration projects.
Experience with energy commodity forecasting, simulation, and risk assessment tools.
Background in LNG or power market analysis.
Familiarity with regulatory frameworks and SOX compliance.
A track record of model validation and implementation in production environments.
Experience with Python-driven automation and system integration projects.
Demonstrated interest in proactive risk management methodologies and AI.

Responsibilities

Construct, validate, and fine-tune market, credit, and liquidity risk models, including VaR, PFE, EaR, funding, and market liquidity models.
Translate algorithms and code from other languages into efficient Python implementations.
Develop valuation models for exotic product validation, including weather derivatives and structured products.
Participate in month-, quarter-, and year-end closing processes.
Adapt risk models to changing business strategies and market conditions.
Conduct scenario analyses, stress tests, and back-testing to assess potential effects of adverse market conditions.
Support quarterly special-topic discussions with risk owners and engage with stakeholders on quarterly and annual risk-management projects.
Lead or contribute to trading-system transformation, systems integration, data analytics, systems upgrades, implementations, and Trading Risk Management automation projects.
Collaborate with cross-functional teams to integrate risk models and support a holistic risk-management approach.
Follow SOX control processes and identify or implement control improvements.
Promote proactive risk management, ethical standards, and company values.
Mentor junior colleagues in quantitative best practices, risk assessment, and Python programming.
Implement prototype models as part of system transformation initiatives.
Perform other job-related duties as assigned.