We are seeking an experienced Quantitative Risk Modeling Specialist with strong exposure to high-frequency trading (HFT) to join our Singapore team. The successful candidate will be responsible for developing, enhancing, and maintaining robust risk models and analytics that support our real-time trading operations across global markets. This role sits at the intersection of quantitative research, risk management, and technology, requiring deep technical skills and an understanding of microstructure and ultra-low latency environments.
Key Responsibilities
- Develop and maintain real-time and intraday risk models tailored to HFT strategies (e.g., market risk, inventory risk, liquidity risk).
- Enhance P&L attribution, stress testing, scenario analysis, VaR/expected shortfall, and factor risk models for short-horizon trading.
- Build tools to monitor signal degradation, slippage, latency-driven risks, and limit breaches in high-frequency environments.
- Collaborate with traders, quants, and engineers to define risk limits and ensure alignment with strategy behavior.
- Improve automated risk controls and kill-switch logic for HFT systems.
- Monitor real-time risk metrics and communicate anomalies to senior leadership.
Qualifications
- Master's or PhD in Quantitative Finance, Mathematics, Physics, Computer Science, Statistics, or related field.
- 58+ years of experience in quantitative risk modeling, quantitative research, or risk analytics within HFT, prop trading, or systematic trading.
- Strong programming skills in Python (NumPy, Pandas, SciPy), and familiarity with C++/Java for model integration.
- Deep understanding of market microstructure, order books, execution risk, and signal/noise behavior at high frequencies.
- Experience building intraday/real-time risk models, stress testing, or limit frameworks for short-horizon trading strategies.
Preferred
- Experience working in a global proprietary trading firm, hedge fund, or electronic market maker.
- Familiarity with risk management for market making, stat-arb, or ultra-low-latency strategies.
- Experience with distributed computing, time-series databases (kdb+/q), or streaming analytics systems.
- Strong communication skills and the ability to work closely with trading and technology teams.
