Quantitative Researcher Intern

About the Role

Engineers Gate (EG) is a leading quantitative investment company focused on computer-driven trading in global financial markets.  We are a team of researchers, engineers, and financial industry professionals using sophisticated statistical models to analyze data and identify predictive signals to generate superior investment returns. EG’s investment teams each focus on their independent strategies while utilizing the firm’s proprietary, state-of-the-art technology and data platform to optimize their alpha research.

We are seeking a highly motivated Quantitative Research Intern to join one of our Portfolio Manager (PM) teams. In this hands-on research role, you will collaborate directly with PMs and engineers to develop alpha signals using large and complex datasets. This opportunity provides exposure to systematic U.S. equity strategies and the chance to apply quantitative skills in a real-world trading environment.

We place a high value on continuous learning and development, and this role represents a unique opportunity to work alongside and learn from experienced team members. If you thrive in a fast-paced, data-driven environment, we encourage you to apply.

Key Responsibilities

• Conduct research on large-scale datasets to identify and validate new alpha signals.

• Assist in designing and testing systematic strategies across U.S. equities.

• Support feature engineering, signal construction, and model evaluation.

• Analyze model performance and help optimize signal robustness and predictive power.

• Present research findings and recommendations to the Portfolio Manager.

Required Skills, Qualifications and Experience

• Currently pursuing a Master’s or PhD in a quantitative field (Computer Science, Mathematics, Engineering, Physics, etc.).

• Strong programming skills in Python and SQL.

• Prior experience or coursework in alpha research or quantitative analysis in U.S. equities markets.

• Experience working with large datasets and applying statistical or machine learning techniques.

• Strong analytical thinking, creativity, and attention to detail.

• Excellent written and verbal communication skills.

• High ownership and growth mindset.

• Ability to thrive in a fast-paced and collaborative environment.
  
  

  Nice to Have:

  • Familiarity with alternative datasets.

  • Exposure to portfolio construction and risk management concepts.

  • Exposure to Europe/Asia equities markets.

  • Familiarity with Linux environments and cloud computing tools.