GBM - Quantitative Dev/Strat - Systematic Rates Trading, New York

Quant Dev/Strat - Systematic Rates Trading

Desk Overview

The Systematic Rates Trading desk sits at the intersection of quantitative research, technology, and market-making execution. The team is responsible for overseeing the systematic trading, pricing, and risk management frameworks for global Rates products (including government bonds, interest rate swaps, and futures). We design, build, and manage real-time pricing engines, algorithmic hedging systems, and execution platforms that operate at scale in highly liquid and volatile markets.

Role Description

This is a high-impact, front-office seat designed for a strong Quantitative Developer / Strat who is a self-driven, highly motivated independent thinker. In this role, you will not just implement pre-defined models; you will actively drive the end-to-end development of trading algorithms, market-making logic, and portfolio optimization tools. 

We are looking for an individual who takes a high amount of ownership over their work, from initial exploratory data analysis to production-grade deployment. You will collaborate closely with traders and quantitative researchers to optimize execution, analyze market microstructure, and build robust, high-performance systems where code quality directly impacts desk P&L.

Responsibilities

• Algorithm Development: Design, develop, and optimize systematic trading algorithms, market-making logic, and real-time algorithmic hedging systems.
• Exploratory Data Analysis (EDA): Conduct rigorous data analysis on massive, high-frequency market datasets to identify pricing anomalies, refine trading signals, and improve execution strategies.
• Market Microstructure & TCA: Analyze Rates market microstructure and build sophisticated Transaction Cost Analysis (TCA) frameworks to minimize slippage, model market impact, and optimize execution performance.
• Portfolio Optimization: Implement and refine mathematical models for portfolio optimization, risk allocation, and real-time risk management.
• System Architecture & Performance: Design and maintain the high-performance, low-latency trading infrastructure and data pipelines powering the systematic Rates business.
• End-to-End Ownership: Proactively identify technical bottlenecks, propose architectural improvements, and take full responsibility for the reliability and scalability of the trading stack.

Who We Look For

We are seeking an exceptional software engineer and quantitative thinker with a "builder" mindset. You should thrive in a fast-paced, collaborative trading floor environment where you are expected to work independently, think critically, and take complete ownership of your projects.

Basic Qualifications

• Education: Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Financial Engineering, Mathematics, or a related quantitative field.
• Core Languages: Expert-level proficiency in C++ or Java (for high-performance, low-latency systems) and Python (for data analysis, prototyping, and scripting).
• CS Fundamentals: Strong foundation in data structures, algorithms, systems programming, and concurrent/multi-threaded application design.
• Engineering Best Practices: Deep understanding of the software development lifecycle, including version control (Git), CI/CD pipelines, testing frameworks, and performance profiling.
• Problem Solving: Exceptional debugging skills and the ability to navigate complex, distributed systems under time-sensitive, live-trading conditions.

Preferred Qualifications

• Domain Knowledge: Strong understanding of Rates products (Treasuries, Swaps, Futures), yield curve modeling, and fixed-income analytics.
• Industry Experience: Prior experience working as a Quant Developer, Strat, or Software Engineer on a systematic trading desk, market-making team, or high-frequency trading (HFT) firm.
• Data Engineering: Experience building and maintaining large-scale time-series databases (e.g., KDB+/q, SQL) and ETL pipelines.
• Quantitative Skills: Familiarity with statistical modeling, optimization techniques, and machine learning libraries in Python.