Credit - Systematic Market Making - Associate

JPMorgan Chase JPMorgan Chase · Banking · Singapore · Commercial & Investment Bank

This role focuses on designing, building, and scaling systematic pricing and execution strategies for corporate bonds and ETFs within a quantitative trading team. It involves applying modern engineering and data science, including machine learning, to real market problems, automating workflows, managing risk, and collaborating with traders, technologists, and researchers. The role requires experience in quantitative trading, risk management, delivering production trading systems, and proficiency in Python and Java, with a background in machine learning applied to market modeling.

What you'd actually do

  1. Automate day‑to‑day workflows for US investment‑grade corporate bond trading across Asia, resolving production and trading issues quickly.
  2. Construct and optimize baskets for Portfolio creation/redemption, and execute primary market workflows to enhance balance sheet usage and profitability.
  3. Manage intraday and end‑of‑day risk for US investment‑grade corporate bonds, within risk limits and controls.
  4. Implement, validate, and maintain systematic pricing models for bond portfolios; enhance and support production trading tools in Python and Java.
  5. Analyze large datasets to identify patterns and trading opportunities; design and deploy algorithms for pricing, execution, and order routing in Asia bond markets.

Skills

Required

  • quantitative trading
  • electronic market making
  • systematic execution for fixed income markets
  • risk management for a bond market-making book
  • Python
  • Java
  • asynchronous and event-driven programming
  • testing
  • CI/CD
  • monitoring in production
  • credit bond pricing and analytical models
  • machine learning applied to time-series or market modeling

Nice to have

  • market data
  • time-series technologies (e.g., kdb+/q)
  • distributed systems
  • low-latency or large-scale systems
  • C/C++
  • time-series analysis
  • optimization
  • back-testing
  • model performance monitoring
  • cross-functional teams across regions and time zones

What the JD emphasized

  • production trading systems end‑to‑end
  • machine learning applied to time‑series or market modeling