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Quant - Risk | Propr.xyz

9 hours, 19 minutes ago
Europe
Full-time
Mid Level
Data Scientist
Software Development
AWS Blockchain Datadog Docker Grafana InfluxDB Kafka Machine Learning NestJS Node.js NumPy Pandas Python REST API SciPy SQL Terraform TimescaleDB TypeScript WebSockets
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SwissBorg

SwissBorg

SwissBorg revolutionizes asset management with Ethereum-powered Cyborg advisors and deep learning algorithms, offering a crypto wealth platform for buying, selling, and earning on 40+ digital assets and 16 fiats across 5 exchanges in one app.

Capital Markets
51-200
Founded 2017
$76M raised
View All Jobs 7

Description

  • Support and enhance a real-time risk engine processing 10,000+ position updates per second across perpetuals, spot assets, and prediction markets.
  • Design and implement portfolio risk metrics, including VaR, stress VaR, expected shortfall, Greeks aggregation, and cross-asset correlations.
  • Build position-limit frameworks covering notional, delta, concentration, leverage, and drawdown constraints.
  • Develop tail-risk models for fat-tailed distributions, regime shifts, and correlation breakdowns.
  • Implement cross-margining, liquidation-price, and maintenance-margin calculation engines.
  • Partner with trading infrastructure teams to maintain under-50ms P99 latency for critical risk calculations.
  • Create real-time risk dashboards and alerts for exposure, PnL attribution, limit breaches, and anomalies.
  • Backtest risk models against historical liquidations and volatile market periods.
  • 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 knowledge of statistics, probability, and risk modeling, including VaR, CVaR/expected shortfall, and stress testing.
  • Proficiency in Python and quantitative libraries including NumPy, Pandas, and SciPy.
  • Experience with real-time risk systems processing 1,000+ updates per second at under-50ms latency.
  • Deep understanding of derivatives pricing, perpetual funding rates, mark-to-market, liquidation mechanics, margin calculations, position sizing, and drawdown controls.
  • Knowledge of portfolio risk metrics, including delta, gamma, vega, correlation matrices, beta hedging, and tail risk.
  • Experience with crypto perpetuals, including cross-margining and liquidation cascades.
  • Familiarity with prediction-market mechanics, including AMMs, Kelly criterion, and order-book dynamics.
  • Experience with time-series analysis, including GARCH, EWMA, regime detection, and autocorrelation.
  • SQL proficiency for aggregating risk data across millions of position updates.
  • Ability to translate complex risk concepts into real-time monitoring systems.
  • Experience with Hyperliquid APIs, prop trading or hedge-fund risk management, blockchain-specific risks, or production risk-service technologies is preferred.

Interested in this position?

Apply directly on the company website

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