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Commodities Data Quantitative Researcher

Balyasny Asset Management
Commodities Data Quantitative Researcher
New York, US
150,000 - 220,000
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Job Description

Balyasny Asset Management is seeking a Quantitative Researcher to work in our industry-leading Commodities Data team. The commodity business unit is one of the newest and fastest growing profit centers at BAM, rapidly onboarding new markets and staff around the globe with ambitions to add physical trading capabilities. This role will be part of a highly collaborative and impactful team helping to grow our Commodities business.

As a Quantitative Researcher, you will be responsible for the following:

· Formulate and implement models for validating and correcting historical prices of commodities assets and derivatives

· Develop methodologies and procedures aimed at improving the quality of data delivered for the Commodities space

· Apply quantitative methods to solve data quality topics.

· Contribute to overall product of the Commodities Data organization, helping the team validate, organize, and model Commodities price data.

· Interact with Portfolio Managers and Risk Management to collect model

requirements and validate model outputs

· Work with Risk Management to help calibrate risk systems, particularly for new products and physical assets.


In order to effectively represent the Company and communicate with clients, the employee must be someone who has:

· At least 5+ years of experience as a commodities quant, strategist, or quantitative risk officer.

· Strong academic background 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

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