Commodity Market Risk Manager: VaR, Stress & Analytics

Selby Jennings; New York (NY); On-site; USD 120,000 - 170,000; Full time

Job summary

Selby Jennings in New York seeks a Market Risk Manager to join its growing risk team. The role focuses on enhancing risk frameworks, analytics, reporting, and models for a diverse commodities portfolio, coordinating with trading desks to assess exposures and advise on risk mitigation.

The ideal candidate has 3+ years in market risk within commodity trading, strong VaR and stress testing knowledge, and proficient Python or SQL skills to analyze large datasets in a fast-paced environment.

Qualifications

• Bachelor's or Master's degree in Finance, Economics, Statistics, Mathematics, or a related field.
• 3+ years of market risk experience within commodity trading, energy trading, or financial markets.
• Strong understanding of VaR and stress testing.
• Strong quantitative skills (Python or SQL) and experience working with large datasets.

Responsibilities

• Monitor daily market risk exposures, including VaR, stress testing, and limit utilization.
• Develop and enhance market risk models, reporting tools, and automated risk processes while analyzing large datasets.
• Partner with traders to assess new products, transactions, and market exposures, providing risk-focused insights and recommendations.
• Support the ongoing development of market risk frameworks, policies, stress testing, and risk mitigation strategies.

Skills

Market risk

VaR

Stress testing

Quantitative skills

Education

Bachelor's or Master's in Finance/Economics/Math

Tools

Python

SQL

Job description

Selby Jennings in New York seeks a Market Risk Manager to join its growing risk team. The role focuses on enhancing risk frameworks, analytics, reporting, and models for a diverse commodities portfolio, coordinating with trading desks to assess exposures and advise on risk mitigation.

The ideal candidate has 3+ years in market risk within commodity trading, strong VaR and stress testing knowledge, and proficient Python or SQL skills to analyze large datasets in a fast-paced environment.