J.P. Morgan is a top-tier liquidity provider in global macro markets, offering competitive pricing across commodities, FX, interest rates and credit. Within the firm we offer a full spectrum of products - from plain vanilla to exotic options; from commodities derivatives & major currencies to emerging market bonds. The Automated Trading Strategies (ATS) group drives systematic trading within this space and is responsible for algorithmic pricing, automated risk management and hedging, and intelligent order execution strategies.

As a trading strategist associate, you are part of the Automated Trading Strategies (ATS) front-office team on the trading floor in Singapore with primary focus on Commodities markets, chiefly Precious and Base metals, Energy, Agricultural and Indices products. This role is also part of the trading team in Singapore where you work closely with the desk to drive revenue and expand the business. 

Job responsibilities

Data analysis to identify patterns and revenue opportunities Research to develop models and methods to solve practical trading problems Back testing and assessing pricing, risk management and execution strategies Reviewing trading performance data and making data driven decisions Maintaining and improving trading software systems and tools Expanding the group’s library of modeling, analytics, and automation tools Resolving day-to-day trading issues

Required qualifications, capabilities, and skills

Minimum 3 year of experience in Commodities quantitative trading or similar roles Bachelor degree in computer science, mathematics, physics, engineering, or other quantitative fields Strong programming skills in Java/C++ or other object-oriented languages Excellent research skills Attention to detail, adaptable, driven and collaborative Strong interest in markets and systematic trading

Preferred qualifications, capabilities, and skills

Foundational knowledge of statistic and data analysis techniques Interest or experience in market microstructure and systematic trading Knowledge of order types, L2 market data and central limit order books Experience with KDB+/q and python