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$139,143–$250,553.
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 - Systematic Strategies (Summer Internship - PhD) joins a team focused on delivering quantitative practices such as risk management, big data analysis, AI, and LLM applications to power investment processes. In this role, you will solve complex problems to enhance trading frameworks and strategies while collaborating with senior members. Depending on the specific track, your daily work involves analyzing textual data using advanced NLP models to develop trading signals, building globally accessible quant trading infrastructure, developing alphas utilizing machine learning, improving risk management framework models, or conducting factor model research for equity tools. You will utilize Python to manage large, complex datasets and build predictive models. Required skills include strong knowledge of probability, statistics, and machine learning, with familiarity in language models like BERT, GPT, and XLNet to address real-world investment problems.
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 on portfolio construction and risk exposures.
Conduct factor model research and build tools for equity factors used across the firm.
Build predictive models using large, complex datasets.
Solve complex real-world problems to improve investment and trading frameworks.
What we're looking for
Must be a PhD student graduating between Winter 2027 and Spring/Summer 2028 in Mathematics, Statistics, Computer Science, or a related quantitative field.
Proficiency in Python programming is required.
Strong knowledge of probability and statistics, specifically regarding 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