5-7 years of quantitative risk experience in a clearinghouse, exchange, or trading firm.
Proven expertise in designing and implementing enterprise-scale risk models.
Deep experience with volatility, correlation, and option pricing for derivatives.
Hands-on experience with market risk modeling and stress testing in a clearing context.
Strong fluency with AI-assisted development and coding, with a critical eye for validation.
Expert-level Python skills, including numerical libraries like NumPy and pandas.
Advanced degree in a quantitative field or equivalent experience.
Responsibilities
Design, implement, and maintain enterprise-scale risk models for market risk and counterparty exposure.
Build and validate volatility and correlation models for derivatives.
Develop and execute stress-testing frameworks for various scenarios.
Design and tune auto-liquidation logic to prevent cascading failures.
Utilize AI tools for model development and rigorously validate outputs against established frameworks.
Monitor model performance in production and iterate based on findings.
Collaborate with engineering and trading teams to integrate risk controls into platform architecture.
Benefits
Collaborative work environment with direct access to trading and engineering teams.
Opportunity to work on cutting-edge risk models in a fast-paced market.
Hands-on role with a focus on production-level coding and model validation.
In-person work setup in New York City, fostering team synergy.
Full Job Description
The Role
We're hiring a mid-level quantitative risk analyst to design and implement enterprise-scale risk models at the heart of our clearing operation. You'll own models for market risk, volatility and correlation of derivatives, stress testing, and automated liquidation - the systems that keep the platform solvent and users protected in fast-moving markets.
This is a hands-on role: you'll be building models in production code, not just specifying them. We expect you to work fluently with AI tools for development and research - and to be the skeptic in the room, pressure-testing AI-generated models and code against well-established risk frameworks before anything ships.
What You'll Do
Design, implement, and maintain enterprise-scale risk models covering market risk, margin, and counterparty exposure for a clearing organization.
Build volatility and correlation models for derivatives, including calibration, backtesting, and ongoing model validation.
Develop and run stress-testing frameworks: historical scenarios, hypothetical shocks, and reverse stress tests.
Design and tune auto-liquidation logic - trigger thresholds, liquidation waterfalls, and safeguards against cascading liquidations.
Use AI tools extensively to accelerate model development, coding, and research - and rigorously validate AI outputs against established risk models (VaR/ES, SPAN-style margining, GARCH-family volatility models, standard correlation frameworks) before deployment.
Monitor model performance in production, investigate breaks, and iterate quickly.
Partner with engineering, trading, and product teams to embed risk controls into platform architecture.
Document model assumptions, limitations, and validation results to an audit-ready standard.
What We're Looking For
5-7 years of quantitative risk experience at a clearinghouse, exchange, prime broker, trading firm, or similar.
Proven expertise designing and implementing risk models at enterprise scale - production systems, not just research prototypes.
Deep experience modeling volatility, correlation, option skews, option pricing and implementing at scale for trad-fi derivatives, perpetuals, and fully collateralized event contracts.
Hands-on experience with market risk modeling, stress testing, and auto-liquidation mechanics in a clearing context.
Strong fluency with AI-assisted development and coding, paired with the judgment to pressure-test AI outputs against well-established risk models and catch what looks plausible but is wrong.
