Quant - Risk
6.0/10
SwissBorg
Not specified
Remote
mid
24 days ago
cryptofintechriskweb3PythonNumPyPandasSciPySQLTypeScriptNode.jsNestJS
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Description
What you'll do
- •Support and enhance the real-time risk engine processing 10k+ position updates/second across perpetuals, spots, and prediction markets.
- •Design and implement risk metrics: portfolio VaR, stress VaR, expected shortfall, Greeks aggregation, cross-asset correlations.
- •Build position limit frameworks: notional caps, delta limits, concentration limits, leverage constraints, drawdown thresholds.
- •Develop statistical models for tail-risk scenarios: fat-tailed distributions, regime switching, correlation breakdowns.
- •Implement margin calculation engines: cross-margining logic, liquidation price models, maintenance margin monitoring.
- •Work closely with trading infrastructure team to ensure less than 50ms P99 latency for risk calculations on critical paths.
- •Create real-time dashboards and alerting systems: exposure heatmaps, PnL attribution, limit breaches, anomaly detection.
- •Backtest risk models against historical liquidation events and high-volatility periods to validate accuracy.
- •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 (VaR, CVaR, ES, stress testing).
- •Proficiency in Python with NumPy, Pandas, SciPy for quantitative analysis and backtesting.
- •Experience with real-time risk systems processing 1000+ updates/second with less than 50ms latency.
- •Deep understanding of derivatives pricing: perpetual funding rates, mark-to-market, liquidation mechanics.
- •Portfolio risk metrics: Greeks (delta, gamma, vega), correlation matrices, beta hedging, tail risk.
- •Experience with crypto perpetuals (funding rates, cross-margining, liquidation cascades).
- •Familiarity with prediction markets (AMM mechanics, Kelly criterion, order book dynamics).
- •Time-series analysis: volatility modeling (GARCH, EWMA), regime detection, 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.
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