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Commodities Quant Jobs (NOW HIRING)

US Bank

Quantitative Strategist - Commodities

New York, NY

$132K - $171K/yr

Support research on commodities markets and develop practical understanding of no-arbitrage pricing and rates modeling techniques under the guidance of senior team leadership. Develop quantitative ...

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Commodities Quant information

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$98K

$169.7K

$259.5K

How much do commodities quant jobs pay per year?

As of Jul 27, 2026, the average yearly pay for commodities quant in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What are Commodities Quants?

Commodities Quants are quantitative analysts who specialize in the commodities markets, such as energy, metals, and agricultural products. They use mathematical models, statistical techniques, and programming skills to analyze market trends, price movements, and risk factors specific to these physical goods. Their work supports trading, risk management, and investment strategies in commodity-focused financial institutions or trading firms. Commodities Quants play a key role in pricing derivatives, optimizing portfolios, and developing trading algorithms tailored to the unique characteristics of commodity markets.

What are the key skills and qualifications needed to thrive as a Commodities Quant, and why are they important?

To thrive as a Commodities Quant, you need advanced quantitative skills, a strong foundation in mathematics or finance, and typically a degree in a quantitative field such as physics, engineering, or statistics. Expertise in programming languages like Python, C++, or R, and experience with statistical modeling software and market data systems are crucial. Analytical thinking, attention to detail, and effective communication help you interpret complex data and convey insights to multidisciplinary teams. These skills enable accurate pricing, risk management, and strategy development in the fast-paced, data-driven commodities markets.

How do Commodities Quants typically collaborate with traders and risk managers in their daily work?

Commodities Quants frequently work closely with traders and risk managers to develop and refine pricing models, analyze market trends, and implement trading strategies. They may spend part of their day discussing market scenarios with traders, running quantitative analyses, or optimizing risk metrics for the desk. This collaborative environment ensures that quantitative insights are directly aligned with trading objectives and risk guidelines, allowing for rapid feedback and iterative improvements to models or strategies. Effective communication and teamwork are essential, as the ability to translate complex quantitative findings into actionable insights often determines the impact of a Quant's work.
More about Commodities Quant jobs
What cities are hiring for Commodities Quant jobs? Cities with the most Commodities Quant job openings:
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Commodities Quant jobs near you
Infographic showing various Commodities Quant job openings in the United States as of July 2026, with employment types broken down into 97% Full Time, 1% Part Time, 1% Contract, and 1% Nights. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.
Commodities Quant Analyst

Commodities Quant Analyst

Verition Group LLC

Houston, TX • On-site

Full-time

Posted 5 days ago

Job description

Verition Fund Management LLC ("Verition") is a multi-strategy, multi-manager hedge fund founded in 2008. Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.
We are a leading multi-strategy hedge fund, is seeking a Quantitative Analyst to join a commodities-focused investment pod in Houston. This is a highly research-oriented role working directly alongside an experienced Portfolio Manager to develop differentiated investment signals using alternative data and quantitative research techniques. The successful candidate will combine a strong foundation in statistics, financial modeling, and Python with a genuine curiosity for uncovering new sources of alpha. Rather than focusing on software engineering, this individual will spend their time researching markets, identifying unique datasets, testing hypotheses, and developing predictive signals that can be incorporated directly into the investment process.
A significant portion of the role will involve sourcing, analyzing, and modeling alternative datasets related to global commodity markets. This includes working with data such as crude oil vessel tracking (AIS), shipping and freight activity, pipeline flows, refinery operations, storage and inventory data, weather, satellite imagery, and other non-traditional datasets. The objective is to transform raw information into robust, statistically validated signals that provide a measurable investment edge.
Responsibilities:
  • Developing financial time series models and predictive forecasting techniques across energy and commodity markets.
  • Researching, evaluating, and incorporating alternative datasets into the investment process.
  • Designing, testing, and validating alpha signals through rigorous statistical analysis and backtesting.
  • Building research pipelines to clean, organize, and analyze large structured and unstructured datasets.
  • Leveraging AI and machine learning techniques to improve feature engineering, accelerate research, and identify differentiated investment opportunities.
  • Collaborating with the Portfolio Manager to rapidly prototype new ideas and continuously refine investment models as market dynamics evolve.

Qualifications:
  • Python and the broader scientific computing ecosystem, including libraries such as Pandas, NumPy, SciPy, and scikit-learn.
  • Financial time series analysis, statistical modeling, feature engineering, and hypothesis testing.
  • Backtesting frameworks, predictive modeling, and signal evaluation.
  • Machine learning techniques and modern AI tools, including large language models, to accelerate quantitative research.
  • SQL, cloud-based data platforms, and experience working with large-scale structured and unstructured datasets.

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