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About the role
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.
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