Quantitative Research Intern

Engineers Gate•New York, NY

1d•$100,000 - $130,000

About The Position

We are seeking a Quantitative Research Intern to join a three-person, fully systematic investment team focused on intraday US equities trading. You will work directly with the portfolio managers and take ownership of a research project from initial data exploration through model development and evaluation, with regular feedback along the way. We are looking for someone who can work independently, develop original hypotheses, and turn a time series dataset into a rigorously tested predictive model. The work involves understanding the data, choosing appropriate methods, and critically assessing whether results are robust and relevant to trading.

Requirements

Currently pursuing a Bachelor’s, Master’s, or Ph.D. in a quantitative field such as Computer Science, Mathematics, Engineering, Physics, Statistics, Finance, Economics, or a related discipline.
Prior finance experience through an internship or full-time role, ideally in quantitative research.
Academic research experience involving quantitative methods, such as a thesis, research assistantship, or substantial research project.
Strong programming skills in Python.
A solid foundation in probability, statistics, and time series analysis, with experience applying statistical modeling or machine learning to data.
Ability to work independently and carry an open-ended research problem from initial exploration through model evaluation.
Clear written and verbal communication

Nice To Haves

Demonstrated interest in quantitative finance beyond coursework or employment, such as independent research, personal modeling projects, competitions, open-source contributions, or relevant student activities
Independently developed models or research ideas you would like to explore with the team

Responsibilities

Clean, validate, and analyze financial time series datasets, including high-frequency data, to establish a reliable foundation for research.
Develop your own research hypotheses and build statistical or machine learning models to identify predictive signals for systematic trading.
Design and run backtests that evaluate performance, robustness, and practical relevance, with careful attention to overfitting and data leakage.
Refine models based on experimental results and feedback from portfolio managers.
Communicate findings, assumptions, limitations, and proposed next steps clearly.
Contribute original modeling ideas and explore promising research directions with the team.