Off-Cycle Trading Intern – Quantitative Researcher

Job Description

Off-Cycle Trading Intern – Quantitative Researcher, 2026, Hong Kong | Millennium

The Tone:
This is a full-time internship at Millennium, located in Hong Kong. Millennium is a global, diversified alternative investment firm, founded in 1989, focused on delivering results for investors through continuous evolution, innovation, and strategic focus. This role is crucial for developing proprietary quantitative models that directly support live trading decisions across APAC and global equity markets, contributing to the firm’s P&L. Millennium empowers its people with both independence and support, fostering an environment for deepening expertise and accelerating development.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person Hong Kong, China

• Team: Part of Millennium’s global trading business that trades APAC and global equity markets from Hong Kong.
• Mission: To develop proprietary quantitative models for return forecasting, portfolio construction, execution, and risk management that support live trading decisions.
• Tech Stack: Python, C++

What You’ll Actually Do
• Conduct Quantitative Research: Conduct quantitative research on trading and investment strategies, primarily focusing on equities, and, where relevant, extending across other financial markets.
• Develop Alpha Signals: Research and develop alpha signals for mid-frequency trading, applying rigorous, data-driven scientific methods to generate actionable insights.
• Analyze Complex Datasets: Analyze various technical, fundamental, sentiment, and alternative datasets to identify and thoroughly evaluate signals that can directly inform alpha strategy development and significantly support the team’s P&L.
• Enhance Research Platforms: Maintain and enhance existing quantitative models, codebase, and research tools, contributing to the continuous improvement of the team’s alpha research platform.

The Must-Haves
• Background: Pursuing a Master’s or PhD in Computer Science, Mathematics, Statistics, Operations Research, Financial Engineering, Financial Economics, or a closely related quantitative field. Candidates should demonstrate a strong academic foundation in one or more areas such as statistics, time series analysis, financial accounting, econometrics, numerical computing, optimization, machine learning, or large language models.
• Experience: Demonstrated hands-on experience working with data, particularly in a context requiring analytical skills.
• Skills: Strong programming proficiency in Python and C++, essential for developing and maintaining quantitative models and tools.
• Bonus: Prior internship experience within the financial industry is preferred, though not strictly required. Experience working with large, unstructured alternative datasets is also a preference. Candidates should also bring high intellectual curiosity, a proactive mindset, and the ability to learn quickly, while working independently and contributing effectively in a team environment.