Quant Risk

This is a fully remote position, open to applicants in Switzerland.

📋 Description

• Assist in the enhancement of the real-time risk engine, processing over 10,000 position updates per second across perpetuals, spots, and prediction markets.

• Design and execute risk metrics including portfolio Value at Risk (VaR), stress VaR, expected shortfall, Greeks aggregation, and cross-asset correlations.

• Establish frameworks for position limits: notional caps, delta limits, concentration limits, leverage constraints, and drawdown thresholds.

• Develop statistical models for tail-risk scenarios, involving fat-tailed distributions, regime switching, and correlation breakdowns.

• Implement engines for margin calculations, including cross-margining logic, liquidation price models, and monitoring of maintenance margins.

• Collaborate closely with the trading infrastructure team to maintain a P99 latency of less than 50 milliseconds for risk calculations on critical paths.

• Create real-time dashboards and alerting systems featuring exposure heatmaps, PnL attribution, limit breaches, and anomaly detection.

• Conduct backtesting of risk models against historical liquidation events and high-volatility periods to ensure accuracy.

• Design and implement circuit breakers and kill switches to handle extreme market conditions or system anomalies.

⛳️ Requirements

• A minimum of 3 years of experience in quantitative risk, trading systems, or financial engineering.

• A solid foundation in statistics, probability theory, and risk modeling (including VaR, CVaR, ES, and stress testing).

• Proficiency in Python, utilizing libraries such as NumPy, Pandas, and SciPy for quantitative analysis and backtesting.

• Experience with real-time risk systems that process over 1,000 updates per second with latency under 50 milliseconds.

• In-depth knowledge of derivatives pricing, including perpetual funding rates, mark-to-market, and liquidation mechanics.

• Familiarity with portfolio risk metrics such as Greeks (delta, gamma, vega), correlation matrices, beta hedging, and tail risk.

• Experience with crypto perpetuals, including funding rates, cross-margining, and liquidation cascades.

• Understanding of prediction markets, focusing on AMM mechanics, the Kelly criterion, and order book dynamics.

• Expertise in time-series analysis, including volatility modeling (GARCH, EWMA), regime detection, and autocorrelation.

• Proficient in SQL for risk aggregation queries across millions of position updates.

• Ability to convert complex risk concepts into effective real-time monitoring systems.

• Knowledge of margin calculations, position sizing, and drawdown controls.

🏝️ Benefits

• Health insurance

• Competitive salary

• Flexible work hours

• Opportunities for professional development