Quantitative Research Intern - Specialist Equities

Requirements

• Prior internship in quantitative research or systematic investing.
• Hands-on experience working with noisy, large-scale financial or alternative datasets.
• How to Apply
• Apply online with your CV and a short cover note. In your cover note, describe one real research or engineering problem you worked on: what you tried, what worked, what didn't, and what you would do differently.
• Inclusion, Work-Life Balance and Benefits at Man Group
• Our compreh

Benefits

Additional Information

About Man Group Man Group is a global alternative investment management firm focused on pursuing outperformance for sophisticated clients via our Systematic, Discretionary and Solutions offerings. Powered by talent and advanced technology, our single and multi-manager investment strategies are underpinned by deep research and span public and private markets, across all major asset classes, with a significant focus on alternatives. Man Group takes a partnership approach to working with clients, establishing deep connections and creating tailored solutions to meet their investment goals and those of the millions of retirees and savers they represent. Headquartered in London, we manage $227.6 billion* and operate across multiple offices globally. Man Group plc is listed on the London Stock Exchange under the ticker EMG.LN and is a constituent of the FTSE 250 Index. Further information can be found at www.man.com * As at 31 December 2025 Location: Hong Kong SAR Duration: 3-4 months, starting 2026 Eligibility: Final year students (Bachelor's, Master's, or PhD) The Team Join the Specialist Equities team, a small and collaborative systematic equity research group focused on developing medium-frequency statistical arbitrage alpha signals. You will receive day-to-day mentorship from a quantitative researcher in Hong Kong, with core supervision and high-level exposure to portfolio managers and senior management in London. Position Overview You will own a research project end-to-end, taking a disciplined scientific approach to form hypotheses, construct signals, and iterate towards deployable outcomes. The project scope is defined on our side and will be closely tailored to your specific background and strengths once we've met you. During the internship, you will: Research time-series methods, advanced statistical techniques, and machine learning models to extract predictive value from complex datasets. Analyze large, noisy, real-world data sets to identify systematic trading opportunities. Help develop statistical and ML-based tools and techniques to solve complex data-related problems throughout the research process. Typical Day Your primary focus throughout the day will be on researching new statistical techniques, exploring datasets, and prototyping signal variants. Apply critical thinking at every step-rigorously backtesting ideas, diagnosing overfitting, and questioning your own assumptions. Discuss and present research results with your immediate team, portfolio managers, and senior management, actively incorporating their feedback. Document your work cleanly and clearly so colleagues can easily build upon your findings. Required Qualifications Quantitative Background: Pursuing a Bachelor's, Master's, or PhD in computer science, statistics, machine learning, signal processing, optimization, mathematics, physics, or a related STEM subject. Research Excellence: Demonstrated ability to conduct high-quality, in-depth, and rigorous research on real-world data, along with the ability to communicate complex results clearly to both technical peers and senior stakeholders. Programming Proficiency: Strong proficiency in Python, with a solid understanding of data structures, algorithmic complexity, and numerical libraries (e.g., pandas, numpy). Statistical & ML Knowledge: Strong grasp of probability, statistics, linear algebra, time-series analysis, and machine learning, including the practical ability to recognize and mitigate overfitting. Team Player: Strong communication skills with the ability to work well in a collaborative, high-pace setting, driving projects to completion across accelerated timelines and coordinating with colleagues across multiple regions.

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