AI-Driven Quantitative Trading & Research Associate

Testing Site - JPMC; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

J.P.Morgan's Quantitative Trading & Research (QTR) Securities Services team seeks an Associate to tackle technically complex problems by applying quantitative methods, ML, and data analytics. You will work with business leads and technical peers to build deployable AI/ML analytics and production-grade code using Python in a cloud/HPC environment.

You will analyze large proprietary datasets, prototype new approaches, and present actionable results to stakeholders, helping shape the firm’s data

Qualifications

• Advanced degree in a quantitative field with strong mathematics background.
• Robust understanding of ML, statistics, and their application to real-world data.
• Experience in tackling data science problems end-to-end using Python.

Responsibilities

• Work with business leads to develop AI/ML-driven analytics and automation that support their business goals.
• Perform large-scale analysis on proprietary datasets to solve problems.
• Write production code to implement ideas and drive business value.
• Make actionable recommendations through presentations to stakeholders.
• Collaborate with Quantitative Research, Technology and CDAO to advance data strategy.

Skills

ML & Statistics

Python programming

Communication

Data analysis

Problem solving

Education

PhD or MS in a quantitative field (Physics/Math/CS/Engineering)

Tools

Python

Cloud computing

HPC

Job description

J.P.Morgan's Quantitative Trading & Research (QTR) Securities Services team seeks an Associate to tackle technically complex problems by applying quantitative methods, ML, and data analytics. You will work with business leads and technical peers to build deployable AI/ML analytics and production-grade code using Python in a cloud/HPC environment.

You will analyze large proprietary datasets, prototype new approaches, and present actionable results to stakeholders, helping shape the firm’s data