Quantitative Developer, Intern
External
Man · Shanghai, ChinaPrepare for this interview
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Requirements
- Prior internship in quantitative development , systematic investing, or front-office technology
- Education
- Currently enrolled at a top-tier university in Computer Science, Software Engineering, Mathematics, Physics, or a related field with strong computational content
- Inclusion, Work-Life Balance and Benefits at Man Group
- Equal Employment Opportunity Policy
- Man Group is a Disability Confident Committed employer; if you require help or information on reasonable adjustments as you apply for roles with us, please contact TalentAcquisition@man.com .
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 Key Skills & Requirements: Core Technical Strong Python proficiency - you'll refactor research code into robust, tested production pipelines daily Awareness of performance and optimisation - profiling, memory management, and computational efficiency when scaling research code across large universes Solid software engineering practices : version control, unit testing, code review, CI/CD, and refactoring - you care about code quality, not just code that works Comfortable on Linux platforms with working knowledge of shell scripting Experience with data analysis libraries (NumPy, SciPy, pandas) and handling large datasets Familiarity with databases and data infrastructure (SQL, data pipelines, or tools like ArcticDB) Exposure to ML/DL frameworks (PyTorch, TensorFlow, scikit-learn) or LLM-related tooling Research & Domain Relevant mathematical knowledge - statistics, optimisation, or factor modeling - enough to understand the research you're productionising A keen interest in financial markets and how quantitative strategies are built and deployed
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