This listing doesn't post a salary. Most similar roles pay
$126,800–$234,150.
Based on 240 similar postings.
Employer
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
59 open roles
on FindRole.
Listed pay typically runs
$175,000–$250,000
across 30 roles with salary data.
The Quantitative Researcher - Quantitative Strategies (Summer Internship) role involves joining the quantitative research team to solve complex problems while enhancing investment and trading frameworks. Interns contribute to various specialized areas including systematic research using advanced NLP models for trading signals, multi-asset arbitrage by building global trading infrastructure, alpha capture utilizing LLM and machine learning methods, risk management through framework model improvements, and portfolio construction via factor model research. The role requires proficiency in Python and a strong foundation in probability, statistics, and machine learning. Candidates must be able to manage large, complex datasets to build predictive models. Preferred technical skills include familiarity with language models like BERT, GPT, and XLNet. This position focuses on the core business problem of improving investment processes through data-driven analysis across diverse asset classes and risk management domains.
Analyze textual data using advanced NLP models to develop actionable trading signals.
Build, support, and integrate globally accessible quantitative trading infrastructure.
Develop alpha signals utilizing LLM and machine learning methods to enhance trading strategies.
Improve risk management framework models and conduct research analysis on portfolio construction.
Conduct factor model research and build tools for equity factors used across the firm.
Solve complex real-world problems to improve investment and trading frameworks.
Process large, complex datasets to build predictive models for quantitative strategies.
What we're looking for
Master's or PhD student graduating between Winter 2027 and Spring/Summer 2028 in Mathematics, Statistics, Computer Science, or a related quantitative field.
Proficiency in Python programming.
Strong knowledge of probability and statistics, including machine learning and natural language processing.
Experience working with large, complex datasets and building predictive models.
Prior independent research experience in a data-driven environment.
Familiarity with language models such as BERT, GPT, and XLNet, and NLP related publications (preferred).
Outstanding analytics skills and attention to detail.
Ability to clearly communicate complex and technical subject matters.
Python
C++
Java
SQL
R
MATLAB
Machine Learning
Data Science
Time-series Analysis
Optimization
Statistical Learning
Tableau
Power BI
Portfolio Construction
Quantitative Research