Quantitative Researcher - Physical Commodities

Responsibilities include:

Formulate and implement models for onboarding new commodity products and derivatives, such as methodologies for constructing volatility surfaces and modeling physical assets.
Be responsible for quality of risk metrics, such as vol calculation methodology.
Develop methodologies and procedures to conduct historical and hypothetical stress testing, as well as analysis of the results using standardized statistical metrics.
Work with Risk Management and Risk Tech to configure and calibrate risk systems, particularly for new products and physical assets.
Apply quantitative methods to solve risk topics, such as estimating market liquidity and liquidation costs.
Contribute to overall risk management team at BAM in risk analytics, processes, and reporting. This may involve ad-hoc risk analysis for portfolios that are not commodities-focused or investigation of impact of a commodities-focused portfolio to the overall risk of the firm.
Contribute to Global Risk Committee's understanding of risk drivers and considerations in related markets.
Improve and extend existing risk reporting tools, including risk analysis, P&L attribution, and portfolio construction, with focus on both regular periodic reporting and ad-hoc requests.

Requirements

At least 5+ years of experience, with 10+ years preferred, as a commodities quant, strategist, or quantitative risk officer, at a physical energy trading firm.
Strong academic background (masters/doctorate) in quantitative fields such as math, physics, engineering, statistics, economics, or finance.
Skilled in valuing and modeling physical commodity assets and structured transactions, such as gas or oil storage and pipeline transport, power tolls, transmission, etc.
Experience with as many of the following commodities as possible: electricity, congestion markets, natural gas, crude oil, oil products, energy assets, agricultural commodities, structured transactions, shipping.
Experience with seasonality in commodities risk models.
Strong programming skills in Python and SQL. Must be familiar with numeric libraries such as pandas, numpy, etc.
Strong problem-solving skills.
Enjoy working in a collaborative environment and able to communicate complex ideas clearly