Quantitative Researcher - Quantitative Strategies (Summer Internship)

About BAM & QR Research

At BAM, our Researchers collaborate across all asset classes, applying a wide range of quantitative practices, including risk management, big data analysis, AI, LLMs, and more. The models built by our QR team power our quantitative strategies and enhance our investment process.

QR Intern

As a QR Intern, you will participate in a hands-on 10-week program designed to take your research abilities to the next level. You will work on complex, real-world problems and make an impact by enhancing our investment and trading frameworks and strategies. The program includes mentorship and collaboration with senior team members, as well as opportunities to expand your network with the broader intern cohort. QR interns will be hired into the Systematic, Multi-Asset Arbitrage, Risk and Portfolio Construction teams.

Research Internship Opportunities

Systematic Research

Interns analyze textual data using advanced NLP models to develop actionable trading signals.

Multi-Asset Arbitrage Research

Interns build, support, and integrate globally accessible quantitative trading infrastructure. They also interact with Portfolio Managers and Quant Researchers to build the toolkits needed for these efforts.

Alpha Capture Research

Interns develop alphas using LLM and machine learning methods to enhance trading strategies within a L/S Equity investment team.

Quant Risk Management

Interns work with Senior Researchers and Risk Managers to improve framework models and conduct research analysis of the investment process, providing insights related to portfolio construction and risk exposures.

Portfolio Construction Research

Interns conduct factor model research and build tools that are essential to the firm’s equity factor research and its use across the organization.

Qualifications

• Master’s or PhD student graduating between Winter 2027 and Spring/Summer 2028, pursuing a degree in Mathematics, Statistics, Computer Science, or a related quantitative field
• Programming proficiency in Python
• Strong knowledge of probability and statistics (including 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 (plus)
• Outstanding analytics skills and attention to detail
• Ability to clearly communicate complex and technical subject matter
• Pragmatic approach and a can-do attitude in solving real-world investment problems
• Results-driven mindset, ability to work in an ambiguous environment, and willingness to collaborate in a team