At BAM, our Researchers collaborate across all asset classes, delivering a wide range of quantitative practices from risk management, big data analysis, AI, LLM, and more. The models built by our QR team power our quantitative strategies and enhance our investment process.
As a QR Intern, you will go through a hands-on 10-week program designed to take your research abilities to the next level. You will have the opportunity to solve complex, real-world problems and make an impact by enhancing our investment and trading frameworks and strategies. Our program offers mentorship and collaboration with senior members of the team in addition to the opportunity to expand your network with the greater intern cohort. QR interns will be hired into our Systematic, Multi-Asset Arbitrage, Risk and Portfolio Construction teams.
Overview of Quant Research Internship Opportunities at BAM:
- Systematic Research interns are tasked with analyzing textual data using advanced NLP models to develop actionable trading signals.
- Multi Asset Arbitrage Research interns are tasked with building, supporting, and integrating globally accessible quant trading infrastructure and interacting with Portfolio Managers and Quant Researchers to build requisite toolkits
- Alpha Capture Research interns are tasked with developing alphas utilizing LLM and machine learning methods to enhance our trading strategies within a L/S Equity investment team.
- Quant Risk Management interns are tasked with working alongside Sr. Researchers and Risk Managers to improve framework models and conduct research analysis of the investment process to deliver insights related to portfolio construction and risk exposures.
- Portfolio Construction Research interns are tasked with conducting factor model research and building tools that are essential to equity factor that are used throughout the firm.
Qualifications:
- PhD student graduating between Winter 2027 and Spring / Summer 2028 that is pursuing a degree in Mathematics, Statistics, Computer Science, or related quantitative field.
- Programming proficiency in Python
- Strong knowledge of probability and stats (ML/NLP)
- 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 is a plus
- Outstanding analytics skills and attention to detail
- Ability to clearly communicate complex and technical subject matters
- Pragmatic and have a can-do attitude in approaching real-world investment problems
- Results driven mindset, ability to work in an ambiguous environment, and work collaboratively within a team environment