QRI is seeking Quantitative Research Interns to support financial modeling initiatives across asset classes leveraging Statistical modeling, Machine Learning, Large Language Models, and Graph-based approaches to influence investment decision-making.
Requirements
- strong programming skills
- skilled in modern analytic techniques (e.g. Python, R, pandas, scikit-learn, etc.)
- Must be able to code in Python
- must have exposure to SQL
- Exposure to machine learning, NLP, and/or LLM a plus
- Some Bond pricing and derivative pricing exposure is preferred
- Some understanding of the mathematics of Network Graphs is also preferred
Responsibilities
- Using mathematic, economic and data science techniques to collect, process, and analyze a variety of proprietary and alternative data sets
- Test hypotheses on how a dataset might be utilized for predicting security returns
- Assist the team strengthening and extending our codebase
- Using internal infrastructure and systems to efficiently extract data and features to build and analyze models
- Data staging, cleaning and wrangling
- We are seeking students who are skilled in modern analytic techniques (e.g. Python, R, pandas, scikit-learn, etc.)
Other
- Currently pursuing a PhD (Class of 2027 or 2028) in a technical field (STEM) or Finance/Economics
- Demonstrated experience/interest/skill in quantitative research and data science analysis.
- Academic or intellectual interest in financial markets.
- Excellent communication, presentation, and writing skills, as well as the ability to clearly articulate findings.
- Candidates must be available for the full duration of the program
