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.

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're looking for intellectually curious, driven students at both the undergraduate and graduate level to join one of our investment teams as a Quantitative Research Intern.

This is a hands-on research role where you'll be working directly alongside experienced researchers and engineers, digging into real data, asking meaningful questions, and contributing to work that has a tangible impact on how we invest. Whether you're drawn to statistical modeling, machine learning, or the challenge of finding signal in noisy financial data, this internship is a chance to apply and sharpen your skills in a rigorous, collaborative environment.

Key Responsibilities

• Clean, validate, and analyze diverse datasets to provide a reliable foundation for global equities alpha research
• Develop alpha factors with economic insights and statistical significance from multi-dimensional data
• Design rigorous backtesting frameworks for performance evaluation and robustness testing
• Assist in designing, testing, and evaluating quantitative models and strategies
• Communicate research findings clearly to team members

Qualifications

• Bachelor’s, Master’s, or Ph.D. from a top-tier university in a quantitative field (Computer Science, Mathematics, Engineering, Physics, Statistics, Finance, Economics, or related)
• Strong programming skills in Python (required); experience with SQL, databases, and Linux environments
• Experience or coursework in quantitative research, data analysis, statistical modeling, or machine learning
• Comfortable working with large datasets and applying statistical or machine learning techniques to financial data
• Strong analytical thinking, creativity, and attention to detail
• Excellent written and verbal communication skills
• Passion for quantitative finance with a high sense of ownership, curiosity, and a growth mindset