Quantitative Researcher- Alternative Data
Key Responsibilities
Alternative Data Research & Signal Development
- Source, evaluate, and onboard alternative datasets relevant to China markets (A-shares, H-shares, ADRs, commodities, rates, etc. as applicable).
- Build repeatable pipelines for data cleaning, normalisation, entity mapping (company/brand/product), and feature extraction.
- Develop predictive signals using alternative data (cross-sectional factors, event signals, risk indicators, regime indicators), and assess robustness across time and market regimes.
- Conduct rigorous validation: leakage checks, survivorship bias checks, latency and revision analysis, stability/decay testing, and crowding/overfitting controls.
Modelling & Portfolio Integration
- Apply statistical learning and machine learning methods (regularized regression, tree-based models, deep learning where appropriate) to improve signal strength and stability.
- Translate research outputs into implementable strategies (e.g., alpha overlays, factor portfolios, event-driven sleeves, intraday or mid-frequency signals).
- Partner with portfolio construction and risk to ensure signals are tradable under constraints (turnover, costs, liquidity, risk exposure, capacity).
Backtesting, Monitoring & Continuous Improvement
- Maintain transparent research documentation and reproducible experiments.
- Work with engineers to productionize research code and build monitoring for signal health, performance attribution, and drift detection.
- Continuously refine data + modelling stack based on live feedback and market evolution.
Requirements
Core Qualifications
- Strong quantitative background in Math/Statistics/Computer Science/Physics/Engineering/Econometrics or related field (BS/MS/PhD).
- Demonstrated experience in alpha research or applied ML for time-series / cross-sectional prediction.
- Proficiency in Python (pandas/numpy/scipy/sklearn); solid software engineering practices (version control, testing, clean code).
- Strong understanding of research hygiene: bias control, out-of-sample testing, proper cross-validation for time series, and realistic cost modelling.
- Ability to work with messy, imperfect datasets and build reliable pipelines under ambiguity.
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