Quantitative Researcher, Systematic Multi-Asset Arbitrage (Summer Internship)

Balyasny Asset Management

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Work type: On-site
Location: New York, NY
Employment: Intern
Posted: 73 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $168k

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About Balyasny Asset Management

Balyasny Asset Management (BAM) is a global multi-manager hedge fund offering diversified investment strategies across equities, macro, commodities, and systematic trading.

Balyasny Asset Management currently has 53 open roles on FindRole.

Listed pay typically runs $175,000–$250,000 across 37 roles with salary data.

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At a glance

TL;DR · Quantitative Researcher, Systematic Multi-Asset Arbitrage (Summer Internship)

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The Quantitative Researcher - Systematic, Multi-Asset Arbitrage (Summer Internship) role places interns within the Systematic, Multi-Asset Arbitrage, Risk and Portfolio Construction teams. During this ten-week program, interns solve complex real-world problems by enhancing investment and trading frameworks and strategies. Specific responsibilities include building and integrating globally accessible quant trading infrastructure, interacting with Portfolio Managers to develop requisite toolkits, and conducting research to improve framework models. Candidates must demonstrate programming proficiency in Python and possess strong knowledge of probability, statistics, and machine learning. The work involves handling large, complex datasets to build predictive models and may include utilizing NLP models or language models like BERT, GPT, and XLNet. The role focuses on delivering insights related to portfolio construction, risk exposures, and developing actionable trading signals.

Skills

Python Machine Learning NLP LLM BERT GPT XLNet Statistics Probability Big Data Risk Management Portfolio Construction Data Analysis

What you'll do

Analyze textual data using advanced NLP models to develop actionable trading signals.
Build and integrate globally accessible quantitative trading infrastructure.
Develop alphas using LLM and machine learning methods to enhance trading strategies.
Improve framework models and conduct research analysis to provide insights on portfolio construction.
Conduct factor model research and build tools essential for equity factors.
Build predictive models using large, complex datasets.
Communicate complex and technical subject matters clearly to stakeholders.

What we're looking for

Must be a Bachelor's or Master's student graduating between Winter 2027 and Spring/Summer 2028.
Must be pursuing a degree in Mathematics, Statistics, Computer Science, or a related quantitative field.
Proficiency in Python programming is required.
Must have strong knowledge of probability and statistics, including machine learning and NLP.
Experience working with large, complex datasets and building predictive models is required.
Prior independent research experience in a data-driven environment is required.
Familiarity with language models such as BERT, GPT, and XLNet, and NLP related publications (preferred).
Ability to clearly communicate complex and technical subject matters is required.

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