Lead Quant Strategist, US IRS Algo Trading

Morgan Stanley; New York (NY); On-site; USD 150,000 - 250,000; Full time

Job summary

Morgan Stanley’s US E-Swaps team seeks a quantitative strategist focused on algorithmic market making to build and operate automated trading strategies for USD interest rate swaps.

The role blends research, hands-on development, and close collaboration with live trading systems, traders, and tech teams to translate market signals into actionable pricing, hedging and execution improvements that impact PnL and market share.

Qualifications

• 4+ years of experience in a quantitative role, ideally within trading, electronic market making, algorithmic trading, or a front‑office environment.
• Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, Physics, Financial Engineering, or a related quantitative field.
• Strong programming skills in Python and working understanding of software development lifecycle and system design.
• Experience working with large or high‑frequency market and trading datasets using KDB/q.

Responsibilities

• Lead initiatives across the full lifecycle of electronic and algorithmic market making, from research and modeling to back‑testing, deployment, live monitoring, and performance optimization.
• Use quantitative methods, including machine learning and statistical modeling, to build, improve, and operate automated trading strategies across pricing, hedging, execution, and risk management.
• Analyze market data, trading behavior, client flow, and execution patterns to identify signals and monetizable opportunities, then translate those insights into practical enhancements to live trading algorithms.
• Monitor algo performance and market conditions to help ensure strategies respond effectively to changing market dynamics.
• Partner with trading and sales to identify opportunities that improve PnL, market share, client coverage, and execution efficiency.
• Collaborate with technology teams to develop solutions that are robust, scalable, and production‑ready.

Skills

Python programming

KDB/q

Quantitative research

SDLC understanding

Front‑office collaboration

Education

Bachelor’s degree in a quantitative field

Tools

Python

KDB/q

Git

Job description

Morgan Stanley’s US E-Swaps team seeks a quantitative strategist focused on algorithmic market making to build and operate automated trading strategies for USD interest rate swaps.

The role blends research, hands-on development, and close collaboration with live trading systems, traders, and tech teams to translate market signals into actionable pricing, hedging and execution improvements that impact PnL and market share.