Quantitative Analyst (Calgary, Alberta)

Musket Corporation - Houston, TX

Full-time - Mid Level

Houston, TX

10,001+ employees

Gasoline Stations and Fuel Dealers

About the position

This position will support Musket Energy Canada, ULC, based in Calgary, Alberta. The role focuses on developing and implementing pricing models, conducting market and signal research, and creating analytical trading tools to generate insights that drive trading decision-making and strategy.

Responsibilities

• Develop and implement advanced quantitative models to analyze crude market movements, volatility, and correlations.
• Collect, clean, and manage large datasets from various sources, ensuring data accuracy and relevance for analysis.
• Collaborate with the analytics team and traders to develop signals and refine trading strategies.
• Conduct research and statistical analyses, develop pipeline flows, inventory balances, and arb economics via various transportation methods.
• Build and develop crude oil balances for the US and Canada, incorporating regional basis with global modeling.
• Maintain and improve Condensate Equalization modeling to evaluate pipeline economics.
• Develop refinery input/output modeling including LP modeling.
• Conceptualize valuation strategies, develop and continuously improve mathematical models, and translate algorithms into code.
• Perform other duties as assigned.

Requirements

• High school diploma or equivalent required.
• Bachelor's degree, Master's degree, or Ph.D. in Finance, Economics, Mathematics, Statistics, Engineering, or a related field required.
• Advanced training in mathematics, statistics, physics, computer science, or another highly quantitative field required.
• Proficiency in probability statistics (e.g., time-series analysis, machine learning, pattern recognition, NLP).
• Hands-on programming experience required.
• 2 or more years in commodities trading space (front or mid-office).
• Quant Finance skills including theoretical and practical knowledge of financial and real options.
• Proficiency in creating and using algorithms to investigate large data or error checking problems.

Nice-to-haves

• Prior experience working in a data-driven research environment preferred.
• A general understanding of optimization theory, linear/non-linear models, machine learning techniques, and various financial models (Risk models & T-Cost models) is a plus.