Quant Risk

SwissBorg

Quantitative Risk professional enhancing real-time risk engines and developing robust statistical models for prop firms. Leveraging blockchain technology to facilitate efficient trading operations.

Posted 4/30/2026full-timeRemote • 🇨🇭 SwitzerlandMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

NumpyPandasPythonSQLSwitching

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Competitive salary
Flexible work hours
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

quantitative riskrisk modelingVaRPythonNumPyPandasSciPySQLtime-series analysisvolatility modeling

Soft Skills

ability to translate complex conceptscollaboration with trading infrastructure team