Quantitative Researcher (Commodities)

Description Summary

Balyasny Asset Management (BAM) is a diversified global investment firm founded in 2001 by Dmitry Balyasny, Scott Schroeder, and Taylor O'Malley. With over $34 billion in assets undermanagement, BAM employs more than 2,000 people across 23 offices in the U.S. and Canada, Europe, the Middle East, and Asia. The firm's investment teams span five strategies, including Equities Long/Short, Fixed Income & Macro, Commodities, Multi-Asset Arbitrage, and Systematic. Balyasny's mission is to deliver to its investors absolute, uncorrelated returns in all market environments.

ROLE OVERVIEW

We are seeking a Junior Quantitative Researcher to support the development of quantitative trading strategies across commodities markets. This role sits at the intersection of data, technology, and quantitative research, with a strong focus on turning raw market and fundamental data into actionable models and trade ideas.

Key Responsibilities

• Data onboarding and cross-functional coordination

• Partner with internal teams including Market Data, Technology, and other research stakeholders to source, validate, and onboard datasets required for strategy development.

  Help define data requirements, monitor data quality, and ensure timely availability of new datasets for research and trading use cases.

  Data cleaning and feature engineering

• Clean, normalize, and maintain raw datasets including price, volume, and fundamental data.

  Build robust data pipelines and apply feature engineering techniques to transform raw data into research-ready inputs.

  Improve data usability, consistency, and reliability for forecasting and strategy development.

• Quantitative research and model development

• Develop and implement statistical forecasting models and quantitative research frameworks for trading strategies.

  Apply advanced statistical and machine learning techniques to financial and commodity market data.

  Evaluate model performance, test hypotheses, and refine signals based on empirical results.

• Strategy development

• Research, develop, and support linear and options-based trading strategies.

  Contribute to idea generation, backtesting, and implementation of quantitative approaches.

  Work closely with portfolio managers and researchers to translate market insights into scalable investment processes.

REQUIREMENTS

Master's degree or Ph.D. in a quantitative discipline such as Mathematics, Physics, Statistics, Computer Science, or a Master's in Financial Engineering or related field.

• 1+ years of prior experience working in commoditiesas a Quantitative Analyst
• 1+ years of prior experience working with AI for automation and/or research
• 1+ years of relevant working experience working with Machine Learning for research

• Strong foundation in mathematics, statistics, and quantitative methods.

  Experience applying advanced statistical techniques and/or machine learning models to financial data.

  Proficiency in Python, with experience building production-quality applications or research infrastructure.

  Advanced knowledge of options is required, including option pricing theory, volatility, Greeks, and options risk management.