Balyasny Asset Management L.P.
Quantitative Researcher - Commodities
Balyasny Asset Management L.P., New York, New York, us, 10261
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Balyasny Asset Management (BAM) operates at the intersection of finance and technology. Our teams bring together portfolio managers, financial analysts, quantitative researchers, and software engineers who work together to identify investment opportunities and generate lasting returns for our investors.
BAM's Commodities Strategies group consists of researchers, analysts, and engineers who research, develop, and execute strategies across different commodities. The team develops sophisticated models for trade execution, replication, portfolio optimization, and alpha generation, leveraging cutting‑edge quantitative infrastructure to analyze discretionary strategies and construct optimal portfolios. Quantitative Research works with each investment strategy at BAM, providing analytical rigor and systematic insights for portfolio construction and risk management decisions.
Within Quantitative Research, the Portfolio Construction team combines advanced mathematical techniques with fundamental market knowledge to optimize portfolio allocation and risk management. The team collaborates with Portfolio Managers across fundamental and semi‑systematic strategies to develop frameworks that maximize risk‑adjusted returns while managing downside exposure.
We seek a versatile and motivated individual with a strong quantitative background, solid coding skills, and good basis in commodities trading. You may come from a quantitative/systematic team at a fund or from a trader, strat, structurer, or quant role on a Commodities QIS desk at a bank. You will join a role at the intersection of research, trading, and technology, supporting a large and complex systematic commodities book.
What You’ll Do
Participate in the day‑to‑day operations of a large systematic commodities book, including trading, performance tracking, and risk management.
Maintain and enhance our Python‑based trading and monitoring infrastructure, which interfaces with multiple BAM APIs and databases.
Conduct research and development on systematic overlays to improve the PnL of existing strategies, with a focus on Portfolio Optimization & Execution Optimization. These overlays can have a significant impact on overall performance.
Balance operational responsibilities with long‑term research projects, contributing to both immediate and strategic objectives.
Required Skills
Education & Experience:
MS or PhD in Mathematics, Economics, Statistics, Physics, Engineering, or a related quantitative field, with at least 3 years of experience in quantitative finance on the sell‑side or buy‑side.
Market Knowledge:
Good understanding of how futures markets work, including order book dynamics, types of orders, associated risks, slippage, and market impact. Actual trading experience is highly valued. Alternatively, experience working closely with traders (e.g., as a strat or structurer for a trading desk) and a strong foundational knowledge of markets, combined with a strong appetite to learn trading, are sufficient.
Risk & Compliance:
Extremely risk‑aware, detail‑oriented, and diligent with compliance issues. You must have an eagerness to always operate on the safe side of the rules.
Intellectual Rigor:
Demonstrated scientific mindset in research and development—no overfitting, no overselling of ideas, and a commitment to robust, reproducible results.
Quantitative & Analytical Skills:
Strong foundation in applied mathematics and financial modeling, with hands‑on experience researching and developing new strategies or optimizations.
Problem‑Solving & Practicality:
Not afraid to dig deep into data, and sometimes search for little issues and resolve mismatches that can have big consequences.
Complexity Management:
Ability to handle abstract, high‑complexity concepts and manage practical, high‑stakes situations quickly, demonstrating sound trading intuition.
Technical Skills:
Excellent coding skills (especially Python), with experience collaborating on large codebases using GitHub and integrating with APIs and databases.
Hybrid Mindset:
Appetite for a mix of operational and research work, with the autonomy to balance long‑term projects and day‑to‑day workflow.
Communication:
Strong communication skills, with the ability to clearly present performance, research results, and new ideas to both technical and non‑technical audiences.
Collaboration:
Team‑oriented, proactive, and able to work effectively in a fast‑paced, collaborative environment.
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Balyasny Asset Management (BAM) operates at the intersection of finance and technology. Our teams bring together portfolio managers, financial analysts, quantitative researchers, and software engineers who work together to identify investment opportunities and generate lasting returns for our investors.
BAM's Commodities Strategies group consists of researchers, analysts, and engineers who research, develop, and execute strategies across different commodities. The team develops sophisticated models for trade execution, replication, portfolio optimization, and alpha generation, leveraging cutting‑edge quantitative infrastructure to analyze discretionary strategies and construct optimal portfolios. Quantitative Research works with each investment strategy at BAM, providing analytical rigor and systematic insights for portfolio construction and risk management decisions.
Within Quantitative Research, the Portfolio Construction team combines advanced mathematical techniques with fundamental market knowledge to optimize portfolio allocation and risk management. The team collaborates with Portfolio Managers across fundamental and semi‑systematic strategies to develop frameworks that maximize risk‑adjusted returns while managing downside exposure.
We seek a versatile and motivated individual with a strong quantitative background, solid coding skills, and good basis in commodities trading. You may come from a quantitative/systematic team at a fund or from a trader, strat, structurer, or quant role on a Commodities QIS desk at a bank. You will join a role at the intersection of research, trading, and technology, supporting a large and complex systematic commodities book.
What You’ll Do
Participate in the day‑to‑day operations of a large systematic commodities book, including trading, performance tracking, and risk management.
Maintain and enhance our Python‑based trading and monitoring infrastructure, which interfaces with multiple BAM APIs and databases.
Conduct research and development on systematic overlays to improve the PnL of existing strategies, with a focus on Portfolio Optimization & Execution Optimization. These overlays can have a significant impact on overall performance.
Balance operational responsibilities with long‑term research projects, contributing to both immediate and strategic objectives.
Required Skills
Education & Experience:
MS or PhD in Mathematics, Economics, Statistics, Physics, Engineering, or a related quantitative field, with at least 3 years of experience in quantitative finance on the sell‑side or buy‑side.
Market Knowledge:
Good understanding of how futures markets work, including order book dynamics, types of orders, associated risks, slippage, and market impact. Actual trading experience is highly valued. Alternatively, experience working closely with traders (e.g., as a strat or structurer for a trading desk) and a strong foundational knowledge of markets, combined with a strong appetite to learn trading, are sufficient.
Risk & Compliance:
Extremely risk‑aware, detail‑oriented, and diligent with compliance issues. You must have an eagerness to always operate on the safe side of the rules.
Intellectual Rigor:
Demonstrated scientific mindset in research and development—no overfitting, no overselling of ideas, and a commitment to robust, reproducible results.
Quantitative & Analytical Skills:
Strong foundation in applied mathematics and financial modeling, with hands‑on experience researching and developing new strategies or optimizations.
Problem‑Solving & Practicality:
Not afraid to dig deep into data, and sometimes search for little issues and resolve mismatches that can have big consequences.
Complexity Management:
Ability to handle abstract, high‑complexity concepts and manage practical, high‑stakes situations quickly, demonstrating sound trading intuition.
Technical Skills:
Excellent coding skills (especially Python), with experience collaborating on large codebases using GitHub and integrating with APIs and databases.
Hybrid Mindset:
Appetite for a mix of operational and research work, with the autonomy to balance long‑term projects and day‑to‑day workflow.
Communication:
Strong communication skills, with the ability to clearly present performance, research results, and new ideas to both technical and non‑technical audiences.
Collaboration:
Team‑oriented, proactive, and able to work effectively in a fast‑paced, collaborative environment.
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