Quant Risk

Build and maintain a low-latency real-time risk engine handling tens of thousands of position updates per second. Develop portfolio risk metrics, Greeks aggregation, correlation models, position limits, margin and liquidation systems, statistical tail-risk models, dashboards, alerting, circuit breakers, and automated kill switches.

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, Greeks aggregation, and cross-asset correlation models.
• Build position limit frameworks covering notional, delta, concentration, leverage, and drawdown thresholds.
• Develop statistical models for tail risk, fat-tailed distributions, regime switching, and correlation breakdowns.
• Implement cross-margining, liquidation price, and maintenance margin calculation engines.
• Ensure sub-50ms P99 latency for critical risk calculations with trading infrastructure.
• Create real-time dashboards and alerts for exposure, PnL attribution, limit breaches, and anomalies.
• Backtest risk models against historical liquidation and high-volatility events.
• Design circuit breakers and kill switches for extreme market conditions and 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, expected shortfall, and stress testing.
• Proficiency in Python, NumPy, Pandas, and SciPy for quantitative analysis and backtesting.
• Experience with real-time risk systems processing at least 1000 updates per second with sub-50ms latency.
• Deep understanding of derivatives pricing, perpetual funding rates, mark-to-market, and liquidation mechanics.
• Knowledge of Greeks, correlation matrices, beta hedging, and tail risk.
• Experience with crypto perpetuals, funding rates, cross-margining, and liquidation cascades.
• Familiarity with prediction markets, AMM mechanics, Kelly criterion, and order-book dynamics.
• Time-series analysis experience including 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.