Quant - Risk | Propr.xyz

Swissborg; Polska; On-site; PLN 180,000 - 320,000; Full time

Benefits offered by this job

Hyperliquid API access

Prop trading background

Blockchain risk knowledge

TypeScript familiarity

Event-driven architecture

TimescaleDB experience

ML anomaly detection

Regulatory knowledge

Monte Carlo simulations

DevOps tools

Job summary

Propr.xyz is building a new Operating System for prop firms, helping them leverage blockchain tech to improve efficiency. We enable perpetual futures, prediction markets, and spot assets, deploying to the largest prop firms worldwide.

ROLE involves implementing risk engines, dashboards, and margin models, with a focus on <50ms latency. You will backtest against volatile periods and enhance risk metrics across crypto markets.

Qualifications

• 3+ years in quantitative risk, trading systems, or financial engineering.
• Strong statistics, probability, and risk modeling (VaR, CVaR, ES, stress testing).
• Proficiency in Python with NumPy, Pandas, SciPy for 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.

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 with trading infra team to ensure <50ms P99 latency for risk calculations on critical paths.
• Create real-time dashboards and alerting: exposure heatmaps, PnL attribution, limit breaches, anomaly detection.
• Backtest risk models against historical liquidation events and high-volatility periods.
• Design circuit breakers and kill switches for extreme market conditions.

Skills

Quantitative risk

Python for analysis

Real-time risk

Derivatives pricing

Time-series analysis

SQL proficiency

ML for anomaly detection

Tools

NumPy

Pandas

SciPy

Job description

Propr.xyz is building a new Operating System for prop firms, helping them leverage blockchain technology to make them more efficient. We enable prop firms to leverage perpetual futures on Hyperliquid, prediction markets, and spot assets. We are actively deploying our technologies to the largest prop firms in the world. The pace is intense, but the journey is exciting. We only hire A-players. You need to be exceptional.

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.

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).