VP, Credit Quant Trading & AI Research

Testing Site - JPMC; New York (NY); Presencial; USD 180.000 - 320.000; Jornada completa

Descripción de la vacante

JPMorgan Chase & Co. in New York seeks a Vice President for its Credit Quantitative Trading and Research team.

You will leverage AI/ML to enhance trading strategies across corporate bonds, bond portfolios, and fixed income ETFs globally, bridging research and production deployment. You will collaborate with traders and researchers to design models, translate market data into insights, and build end-to-end tools that scale in a fast-paced, data-driven environment, with emphasis on robustness and

Formación

• A post-graduate degree in a STEM discipline with hands-on AI/ML timeseries experience.
• Experience in quantitative research or related role within e-trading space (3–5 years).
• Strong knowledge of corporate credit, fixed income markets, and ETFs.

Responsabilidades

• Invest in and improve AI/ML models and frameworks.
• Collaborate with market makers, traders, and stakeholders to support trading activities.
• Analyze market trends and datasets to translate into actionable insights.
• Design and implement end-to-end tools and systems ensuring compliance with best practices.
• Foster understanding of cross-functional connections to drive team success.
• Drive projects with a quantitative research perspective toward long-term goals.

Conocimientos

AI/ML

Time series modeling

Statistical modeling

Corporate credit knowledge

Fixed income markets

ETFs knowledge

Python

Java

Communication skills

Collaborative work

Educación

Postgraduate STEM degree

Herramientas

Python

Java

Descripción del empleo

JPMorgan Chase & Co. in New York seeks a Vice President for its Credit Quantitative Trading and Research team.

You will leverage AI/ML to enhance trading strategies across corporate bonds, bond portfolios, and fixed income ETFs globally, bridging research and production deployment. You will collaborate with traders and researchers to design models, translate market data into insights, and build end-to-end tools that scale in a fast-paced, data-driven environment, with emphasis on robustness and