Quantitative Trading & Research Internship (Off-Cycle)

JPMORGAN CHASE BANK, N.A.

Singapore

On-site

SGD 120,000 - 180,000

Full time

4 days ago
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Job summary

JPMorgan Chase & Co. invites applications for an associate in Quantitative Trading & Research (QTR) in Singapore.

This role offers exposure across derivatives pricing, risk management, electronic execution and data‑driven trading analytics, leveraging machine learning to enhance sales, client engagement, portfolio optimization and market making. You will partner with traders, risk managers and marketers to drive pricing, risk management and product innovation while developing scalable models and

Qualifications

  • Pursuing a Master’s in a quantitative field with expected graduation after December 2027.
  • Strong programming ability in at least one language (Python/Java/JavaScript/C++, C#).
  • Demonstrated quantitative and problem‑solving skills and research ability.
  • Excellent communication of complex ideas to both technical and non‑technical audiences.

Responsibilities

  • Contribute to Derivatives Pricing, Risk Management and Electronic Execution analytics.
  • Collaborate with traders, marketers and risk managers to drive pricing, risk and innovation.
  • Develop and implement mathematical models for pricing, hedging, and risk measurement of derivatives.
  • Apply machine learning to pricing, hedging, and idea generation; research high‑frequency strategies.

Skills

Python
C++
Problem solving
Communication

Education

Master’s in Mathematics/Physics/Engineering/CS/ML/Statistics

Tools

Python
C++

Job description

JPMorgan Chase & Co. invites applications for an associate in Quantitative Trading & Research (QTR) in Singapore.

This role offers exposure across derivatives pricing, risk management, electronic execution and data‑driven trading analytics, leveraging machine learning to enhance sales, client engagement, portfolio optimization and market making. You will partner with traders, risk managers and marketers to drive pricing, risk management and product innovation while developing scalable models and

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