Quantitative Research Intern

Engineers Gate; New York (NY); On-site; USD 100,000 - 130,000; Part time

Job summary

Engineers Gate in New York invites a Quantitative Research Intern to join a small, fully systematic team focused on intraday US equities trading. You will work directly with portfolio managers and own a research project from data exploration to model development and evaluation.

The role requires independence, original hypotheses, and robust backtesting with attention to overfitting. Strong Python skills are essential, and you will communicate findings, limitations, and next steps clearly to the

Qualifications

• Pursuing a Master’s or Ph.D. in a quantitative field such as CS, Math, Engineering, Physics, Stats, Finance, Economics, or related.
• Finance internship or research experience desirable.
• Academic research involving quantitative methods (thesis, RA, or substantial project).
• Strong programming skills in Python.

Responsibilities

• Clean, validate, and analyze financial time series datasets, including high-frequency data.
• Develop hypotheses and build statistical or ML models for systematic trading.
• Design and run backtests evaluating performance, robustness, and relevance.
• Refine models based on experimental results and PM feedback.
• Communicate findings, assumptions, limitations, and next steps clearly.
• Contribute original modeling ideas and explore research directions with the team.

Skills

Independent research

Communication

Python programming

Education

Master’s or Ph.D. in quantitative field

Tools

Python

Job description

About EG

Engineers Gate (EG) is a leading investment manager founded in 2014 as a quantitative, computer-driven trading firm. Today, EG operates as a diversified, multi-strategy investment platform that combines systematic research with selective discretionary approaches.

Engineers Gate (EG) is a leading investment manager founded in 2014 as a quantitative, computer-driven trading firm. Today, EG operates as a diversified, multi-strategy investment platform that combines systematic research with selective discretionary approaches.

EG's multi-manager platform allows independent investment teams to pursue distinct strategies while benefiting from shared infrastructure, risk management, and operational support. The firm’s collaborative groups of researchers, engineers, and investment professionals deploy sophisticated statistical models, proprietary technology, and a centralized data platform to isolate and solve challenging problem sets in the global financial markets.

About The Role

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.

Key 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.

Qualifications

• Currently pursuing a 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.

Preferred Qualifications

• 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.

The annualized base salary for this role is anticipated to be $100,000–130,000, prorated for the duration of the internship, excluding potential bonuses, additional compensation, and benefits. Actual compensation will depend on various factors including skills, experience, and qualifications.