Quantitative Researcher

GQR; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

GQR is expanding its fully systematic research platform in New York. We are seeking Quantitative Researchers to join a highly quantitative environment focused on developing systematic trading strategies across Rates, Fixed Income, and Credit.

You will work across the full research lifecycle—from signal identification and model development to backtesting and implementation—driving research across asset classes with strong Python/C++ skills and a quantitative background.

Qualifications

• Strong quantitative research or systematic trading experience.
• Experience working with financial markets and market data.
• Strong Python and/or C++ skills.
• Background in statistics, mathematics, computer science, engineering, physics, or a related quantitative field.
• Experience researching systematic strategies in any asset class.

Responsibilities

• The team works across the full research lifecycle, from identifying signals and developing models to backtesting, portfolio construction, and implementation.

Skills

Quantitative research

Systematic trading

Market data analysis

Python

C++

Statistics

Job description

A leading investment bank is expanding its fully systematic trading platform in New York and is looking for Quantitative Researchers to join the team.

This is a highly quantitative research environment focused on developing systematic trading strategies across areas including:

• Rates
• Fixed Income
• Credit

The team works across the full research lifecycle, from identifying signals and developing models to backtesting, portfolio construction, and implementation.

Looking for:

• Strong quantitative research or systematic trading experience
• Experience working with financial markets and market data
• Strong Python and/or C++ skills
• Background in statistics, mathematics, computer science, engineering, physics, or a related quantitative field
• Experience researching systematic strategies in any asset class