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Quantitative Researcher

Man Group
CompanyMan Group
CategoryScience & Research
LocationShanghai
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted25 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
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 $228.7 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 At Man Group, we respect your privacy and we are committed to protecting and safeguarding your Personal Data. We have developed policies and processes which are designed to provide for the security and integrity of your Personal Data. We are committed to Processing your Personal Data fairly and lawfully, and being open and transparent about such Processing. For further information on how we process your data, please see the privacy notice for applicants  here * As at 31 March 2026   Macro: Key competencies Advanced degree in Computer Science, Statistics, Mathematics, Finance/Economics or related quantitative field from a top-tier institution Passion in investment research with strong intuition and the ability to think-out-of-the-box; strong record of original research and demonstrated problem solving ability Excellent quantitative skills from training in econometrics or statistics, and extensive experience in utilizing those skills in empirical research. Practical exposures to LLMs and AI agents a plus Strong programming skills analyzing large and complex data with statistical tools (Python) Proficiency in risk parity portfolio construction, volatility modeling, derivatives and leverage instruments; knowledge of macro and cross-asset dynamics in China Confident communicator, able to explain complex ideas in simple terms to both external and internal stakeholders 2-5 years of experience in researching and live trading alpha signals for futures in Chinese market   What you will be doing Develop and improve  risk-parity and macro quantitative investment strategies  across financial markets within the dedicated China research team. Take ownership of the full investment lifecycle — from  alpha generation and portfolio construction through to trade execution and implementation  — with a particular focus on  risk-balanced allocation frameworks across asset classes . Work closely with team members to create and support strategies by: Alpha Generation & Strategy Development:  Identify new investment ideas and innovative data sources across  macro themes, asset classes, and risk factors  relevant to risk-parity frameworks. Data & Modeling:  Gather and refine complex datasets; apply statistical analysis and quantitative modeling to build and improve  risk models, correlation frameworks, volatility estimation, and return forecasting  models central to risk-parity construction. Portfolio Construction & Implementation:  Participate in research on  risk-budgeting methodologies, dynamic leverage, diversification, and rebalancing strategies , as well as trading cost models and execution optimization. Interpretation & Decision-Making:  Synthesize and interpret model outputs to drive portfolio decisions, with the ability to clearly communicate the  rationale, risk exposures, and performance attribution  of strategies to internal stakeholders. External Represent
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