Equities Quant Research Intern — Signals & Backtesting

Socket.dev; Chicago, New York (IL, NY); Hybrid; USD 24,000 - 38,000; Full time

Job summary

DV Group seeks a 2027 Quantitative Research Intern to join our equities team. You will explore market data, develop predictive signals, and build models that inform trading decisions. Work alongside senior researchers to gain hands-on, real-world exposure.

You will work with Python data libraries to backtest ideas and contribute to data pipelines for high-frequency and time-series data, within a collaborative, fast-paced environment.

Qualifications

• Pursuing a quantitative degree (BSc/MSc/PhD).
• Expected graduation in 2027 or 2028.
• Strong Python skills with data libraries.
• Curiosity about financial markets & microstructure.
• Solid statistics & quantitative analysis foundation.
• High-frequency or signal-generation experience a plus.
• Ability to communicate technical concepts clearly.
• Self-motivated, collaborative in a fast-paced team.

Responsibilities

• Analyze market data to identify patterns and signals across horizons.
• Build and backtest quantitative models using historical data.
• Apply statistics and ML techniques to improve signal quality.
• Collaborate with traders and researchers to deploy strategies.
• Develop and maintain data pipelines for large-scale market data.
• Iterate on research with backtests and team feedback.

Skills

Python

Data analysis

Statistics

Quantitative reasoning

Communication

Machine learning basics

Education

Bachelor's degree

Master's degree

PhD (in progress)

Tools

Pandas

NumPy

SciPy

Job description

DV Group seeks a 2027 Quantitative Research Intern to join our equities team. You will explore market data, develop predictive signals, and build models that inform trading decisions. Work alongside senior researchers to gain hands-on, real-world exposure.

You will work with Python data libraries to backtest ideas and contribute to data pipelines for high-frequency and time-series data, within a collaborative, fast-paced environment.