Quantitative Researcher — Signals & Production-Ready Analytics

Engineers Gate; New York (NY); On-site; USD 130,000 - 200,000; Full time

Job summary

Engineers Gate is seeking a motivated Quantitative Researcher to join a small, collaborative team managing a global, mid-frequency statistical arbitrage portfolio.

You will explore alternative datasets, develop trading signals, and help improve portfolio construction, risk, and transaction cost models while delivering production-grade code.

Candidates typically have 2–5 years of experience in quantitative research, a strong mathematical background, and a degree in engineering, CS, or physics.

Qualifications

• Academic background in Mathematics, Physics, Computer Science, Engineering or a related field.
• Strong mathematical and modeling skills (optimization theory preferred).
• Proficiency in coding (Python, C++ preferred).
• 2-5 years of working experience in quantitative research in equities/futures.

Responsibilities

• Explore unique alternative datasets to develop novel trading signals.
• Develop and enhance frameworks for portfolio construction.
• Utilize state of the art machine learning and data science techniques to improve stock level insights.
• Enhance the team's array of risk and transaction cost models.
• Write robust and production quality code.
• Manage the full pipeline of research projects from idea generation to implementation.

Skills

Python

C++

Mathematics

Statistical modeling

Machine learning

Data analysis

Education

Master's or PhD in a quantitative field

Bachelor's degree in Mathematics, Physics, CS, Engineering or related field

Job description

Engineers Gate is seeking a motivated Quantitative Researcher to join a small, collaborative team managing a global, mid-frequency statistical arbitrage portfolio.

You will explore alternative datasets, develop trading signals, and help improve portfolio construction, risk, and transaction cost models while delivering production-grade code.

Candidates typically have 2–5 years of experience in quantitative research, a strong mathematical background, and a degree in engineering, CS, or physics.