Quant Researcher – Treasury Optimization & Funding

Hudson River Trading; New York (NY); On-site; USD 150,000 - 200,000; Full time

Job summary

Hudson River Trading in New York is seeking a Quantitative Researcher focused on Treasury Optimization and Research to join our PostTrade team. You’ll work with Funding, Finance, and Treasury Engineering to develop optimization models and enhance funding efficiency across global markets, while building scalable analytics that inform decision making.

The ideal candidate is a brilliant quantitative mind with deep math, strong Python skills, and experience in optimization or ML libraries.

Qualifications

• Bachelor’s, Master’s, or PhD in a quantitative field such as CS, engineering, math, stats, physics, OR, or related field.
• Strong problem-solving ability with a foundation in mathematical, statistical, or optimization fundamentals.
• Proficient in Python; experience with numerical libraries is a plus.
• Excellent cross-functional communication with engineers, researchers, and finance professionals.

Responsibilities

• Build and enhance the Treasury Optimization Platform with optimization models to improve funding efficiency and collateral allocation.
• Research funding and collateral modeling; identify opportunities to maximize returns.
• Collaborate with Funding, Finance, and Engineering to deploy research into production systems and streamline cash and collateral workflows.

Skills

Python

Strong problem-solving

Optimization fundamentals

Cross-functional collaboration

Gradient descent

Linear programming

Convex programming

Education

Bachelor’s, Master’s, or PhD in a quantitative field such as CS, engineering, mathematics, statistics, physics, operations research, or related

Tools

NumPy

Pandas

PyTorch

TensorFlow

Job description

Hudson River Trading in New York is seeking a Quantitative Researcher focused on Treasury Optimization and Research to join our PostTrade team. You’ll work with Funding, Finance, and Treasury Engineering to develop optimization models and enhance funding efficiency across global markets, while building scalable analytics that inform decision making.

The ideal candidate is a brilliant quantitative mind with deep math, strong Python skills, and experience in optimization or ML libraries.