Quantitative Research Intern - High-Frequency Trading & ML

Risk Hub; Singapore; On-site; SGD 58,000 - 83,000; Full time

Benefits offered by this job

Competitive pay

Free meals & snacks

Networking events

Mentorship program

Mentors from alma mater

Job summary

Tower Research Capital in New York City offers an internship for undergraduate and graduate students in computer science, mathematics, physics, machine learning, or related fields. You will work on mid- to high-frequency trading algorithms, analyze market data and microstructure, and build tools to analyze data for trading insights.

The role emphasizes hands-on learning within a world-class quantitative trading platform, with collaboration across engineering and trading teams to solve

Qualifications

• Bachelor’s or Master’s student in CS/math/physics/ML or related fields.
• Proficient in an object-oriented programming language (C++ and Python preferred).
• Working knowledge of Linux/Unix and strong problem-solving abilities.
• Interest in financial markets and ability to multitask in a fast-paced environment.

Responsibilities

• Designing, implementing, and deploying mid- to high-frequency trading algorithms.
• Exploring trading ideas by analyzing market data and market microstructure for patterns.
• Creating tools to analyze data for patterns.
• Contributing to libraries of analytical computations to support market data analysis and trading.
• Developing, augmenting, and calibrating exchange simulators.

Education

Bachelor’s or Master’s student

Proficient in C++

Proficient in Python

Strong communication skills

Interest in financial markets

Tools

Linux/Unix

Job description

Tower Research Capital in New York City offers an internship for undergraduate and graduate students in computer science, mathematics, physics, machine learning, or related fields. You will work on mid- to high-frequency trading algorithms, analyze market data and microstructure, and build tools to analyze data for trading insights.

The role emphasizes hands-on learning within a world-class quantitative trading platform, with collaboration across engineering and trading teams to solve