Quant Risk

You will build and maintain a low-latency real-time risk engine that handles tens of thousands of position updates per second. You will design and implement portfolio risk metrics (VaR, CVaR, expected shortfall), Greeks aggregation, and cross-asset correlation models. You will create position limit frameworks, margin calculation engines, circuit breakers, and liquidation models. You will backtest risk models against historical liquidation and high-volatility events, develop statistical models for tail-risk and regime detection, and implement real-time dashboards and alerting for exposure, PnL attribution, and limit breaches. You will collaborate with trading infrastructure to meet strict latency targets and ensure reliable monitoring and automated kill switches under extreme market conditions.

Responsibilities

• Support and enhance the real-time risk engine processing 10k+ position updates per second across perpetuals spots and prediction markets
• Design and implement portfolio VaR stress VaR expected shortfall and Greeks aggregation
• Build position limit frameworks including notional caps delta limits concentration limits leverage constraints and drawdown thresholds
• Develop statistical models for tail risk including fat tailed distributions regime switching and correlation breakdowns
• Implement margin calculation engines with cross margining logic liquidation price models and maintenance margin monitoring
• Ensure sub 50ms P99 latency for risk calculations on critical paths in collaboration with trading infrastructure
• Create real-time dashboards and alerting systems for exposure heatmaps PnL attribution limit breaches and anomaly detection
• Backtest risk models against historical liquidation events and high volatility periods
• Design circuit breakers and kill switches for extreme market conditions or system anomalies

Requirements

• 3+ years of experience in quantitative risk trading systems or financial engineering
• Strong foundation in statistics probability theory and risk modeling including VaR CVaR ES and stress testing
• Proficiency in Python with NumPy Pandas and SciPy for quantitative analysis and backtesting
• Experience with real-time risk systems processing 1000+ updates per second with sub 50ms latency
• Deep understanding of derivatives pricing including perpetual funding rates mark to market and liquidation mechanics
• Knowledge of portfolio risk metrics including Greeks correlation matrices beta hedging and tail risk
• Experience with crypto perpetuals including funding rates cross margining and liquidation cascades
• Familiarity with prediction markets AMM mechanics Kelly criterion and order book dynamics
• Time series analysis experience including volatility modeling GARCH EWMA regime detection and autocorrelation
• SQL proficiency for risk aggregation queries across millions of position updates
• Ability to translate complex risk concepts into real-time monitoring systems
• Understanding of margin calculations position sizing and drawdown controls