swissborg - Quant - Risk | Propr.xyz
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 <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. • Bonus • Experience with Hyperliquid API (WebSocket feeds, vault risk monitoring, liquidation engine). • Background in prop trading, market making, or hedge fund risk management (2-sigma+ shops preferred). • Knowledge of blockchain-specific risks: oracle failures, MEV, liquidation cascades, network congestion. • Proficiency with TypeScript, Node.js, NestJS for building production risk services. • Experience with event-driven architectures, message queues (Redis Streams, Kafka), CQRS patterns. • Time-series databases (TimescaleDB, InfluxDB) for storing tick-level risk snapshots. • Machine learning for anomaly detection: isolation forests, autoencoders, change point detection. • Understanding of regulatory frameworks (CFTC, SEC, MiFID II) and compliance monitoring. • Experience with Monte Carlo simulations, copula models, or extreme value theory. • Published research or contributions to quantitative finance / risk management literature. • DevOps: Docker, AWS (ECS, Aurora), Terraform, monitoring tools (Grafana, Datadog).
Responsibilities
• 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 <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.
Benefits
• "exceptionalNote": "What makes you exceptional?", • "telegramHandle": "@yourhandle", • "appUid": "optional-trading-terminal-uid"
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