Hybrid Quantitative Researcher Intern bei Engineers Gate
Engineers Gate · New York City, Vereinigte Staaten Von Amerika · Hybrid
- Optionales Büro in New York City
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 investment teams. In this hands-on research role, you will collaborate with experienced team members to explore data, develop predictive models, and support research initiatives. This opportunity provides practical experience applying quantitative skills in a dynamic, real-world environment.
We value continuous learning and development, and this role offers the chance to work closely with professionals across research, engineering, and investment teams. If you thrive in a fast-paced, data-driven setting, we encourage you to apply.
Key Responsibilities
Conduct research on large and complex datasets to identify trends, patterns, or predictive signals
Assist in designing, testing, and evaluating quantitative models or strategies
Support feature engineering, data analysis, and model validation
Analyze results and contribute to recommendations for research or strategy development
Communicate research findings clearly to team members
Required Skills, Qualifications, and Experience
Currently pursuing an undergraduate or graduate degree in a quantitative field (Computer Science, Mathematics, Engineering, Physics, etc.)
Strong programming skills in Python and SQL
Experience or coursework in quantitative research, data analysis, or modeling
Comfortable 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, curiosity, and a growth mindset
Ability to thrive in a fast-paced and collaborative environment