You will be joining their technology arm, with AL/ML as its cornerstone, and is committed to providing users with high-quality and stable trading services. The company now has a number of experienced quantitative researchers, world-class deep learning scientists and engineers from leading internet companies and top universities.
The company has also provided various kinds of trading solutions for a number of leading brokerage firms and organizations. The company's vision is to integrate artificial intelligence technology with quantitative investment scenarios, relying on strong artificial intelligence R&D capabilities and advanced trading strategy models, to provide users with comprehensive and stable investment service.
Roles & Responsibilities:
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Research and develop systematic trading strategies across global markets
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Extract signals from market, microstructure, fundamental, and alternative datasets to build quantitative models
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Design and implement robust backtesting and research pipelines
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Build and maintain data infrastructure, including:
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Data ingestion, cleaning, normalization, and standardization
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Security master and identifier mapping (ISIN, CUSIP, SEDOL, RIC, Bloomberg)
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Integrate data with OMS/EMS and prime broker systems for trading and reconciliation
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Monitor and improve data quality and model performance, resolving anomalies and ensuring reliability
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Collaborate closely with PMs, quants, and traders to translate research into production
Qualifications:
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Degree in Computer Science, Mathematics, Engineering, Finance or a related field.
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Experience in quant research, data engineering, or systematic trading in a hedge fund / asset manager / prop firm.
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Strong programming skills using Python / C++ or others.
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Familiarity with global equity markets and financial data
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Strong problem-solving ability, attention to detail, and learning agility
Apply for this job
We are an inclusive organisation and actively promote equality of opportunity for all with the right mix of talent, skills, and potential. We welcome all applications from a wide range of candidates. Selection for roles will be based on individual merit alone.
