What You'll Do
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Assist senior researchers in developing and backtesting factor models using Python/PySpark
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Clean and analyze terabyte-scale datasets (tick data, fundamental data, alternative data)
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Implement prototype trading signals under guidance using our in-house ML framework
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Conduct daily performance attribution analysis on live portfolios
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Research market microstructure patterns in APAC equity dark pools
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Participate in weekly journal clubs discussing latest arXiv quant finance papers
Who We Want
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Bachelor's/Master's in Financial Engineering, Statistics, or CS from target universities
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1+ years hands-on experience with:
✅ Python quant stack (pandas, NumPy, scikit-learn)
✅ Basic probability/statistics (time series analysis, hypothesis testing)
✅ SQL/Spark for big data processing -
Demonstrated mathematical intuition through:
▶️ Kaggle competition rankings (top 20%)
▶️ Quant internship projects with measurable results
▶️ Published research/blog posts on financial modeling -
Basic understanding of equity derivatives (futures, options)
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TOEIC 850+ or equivalent English proficiency