Asset Management - Investment Platform Trading Analytics & Strategy (GFICC) - Executive Director
JPMorgan Chase · New York, United States · 3mo ago
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.
The Trading Research team delivers quantitative analysis across equites, fixed income, credit, currencies, and commodities to drive execution excellence and systematic trading innovation. Our work spans transaction cost analysis (TCA), execution optimization, and automation of trading workflows — all underpinned by a global analytics platform shared across teams in New York, London, Hong Kong, and Mumbai. We partner closely with trading desks to translate quantitative insight into tangible improvements in execution quality and operational efficiency.
Job summary:
As an Investment Platform Quantitative Researcher Executive Director on the Asset Management Investment Platform Trading Research team Executive Director on the Asset Management Investment Platform you will take a leading role in shaping and executing strategy and automation across GFICC asset classes. You will lead cross functional projects for our investment platform and provide execution consultancy to our clients.
Job responsibilities:
- Execution Optimization & Automation Work directly with traders to automate workflows, reduce manual intervention, and improve the speed and consistency of execution — with a particular focus on liquidity-constrained or bespoke instruments such as credit and securitized products.
- Quantitative Trading Research Lead research into market microstructure, price formation, and liquidity dynamics across rates, credit, FX, and commodities. Identify actionable signals and develop statistical frameworks to optimize execution timing, venue selection, and order routing.
- Execution Consultancy Act as a subject matter expert for traders and portfolio managers, providing data-driven recommendations on execution strategy. Advise on optimal approaches to balancing market impact and transaction costs across varying liquidity regimes and instrument types.
- Trade Analytics & Reporting Build and maintain dashboards, TCA frameworks, and execution quality tools that provide transparency into trading performance, market impact, and slippage. Develop both scheduled and bespoke reporting to support desk leadership and portfolio management.
Cross-functional Leadership Coordinate across trading, portfolio management, and technology teams to deliver end-to-end analytics solutions. Define requirements, manage delivery timelines, and ensure outputs are practical and embedded in day-to-day trading workflows.
Required qualifications, skills and capabilities:
Python Proficiency: Strong hands-on experience with Python for quantitative research, including data manipulation (pandas, NumPy), visualization (matplotlib, seaborn), and performance-oriented coding practices.
Market Data & Databases: Demonstrated ability to work with tick-level and transactional market data using high-performance query tools and financial databases.
GFICC Market Knowledge: Solid understanding of fixed income and FX market microstructure, execution dynamics, liquidity provisioning, and the mechanics of electronic and voice trading.
Execution & Algorithms: Practical knowledge of algorithmic execution, smart order routing, and the quantitative drivers of transaction costs in fixed income or currency markets.
Stakeholder Communication: Proven ability to translate complex quantitative outputs into clear, actionable recommendations for non-technical audiences including traders and senior business stakeholders.
Collaborative Development: Experience working in team-based development environments using version control systems (Git) and structured code review processes.
Applied Machine Learning: Familiarity with ML techniques relevant to trading research, including supervised/unsupervised learning, reinforcement learning, or NLP applied to financial data.