Member of Global Risk Management, Quantitative Financial Risk
Develops quantitative tools and scalable Python pipelines for credit, market, and liquidity risk analysis at a digital asset firm. Requires 8+ years in quantitative finance, advanced degree, and expertise in stress testing and real-time risk monitoring.
Salary not listed
Remote8+ YOEData Engineering
About the role
Responsibilities
Design, build, and maintain quantitative analysis tools for credit, market, and liquidity risk assessment
Build scalable analytics pipelines in Python to automate reporting, data transformation, and real-time risk monitoring
Execute portfolio margin stress tests and scenario analysis under tight timelines, delivering actionable insights to stakeholders
Lead end-to-end development of quantitative analysis tools from problem definition through production deployment with minimal oversight
Run ad-hoc real-time analysis and deliver under pressure
Navigate ambiguous risk problems by selecting appropriate quantitative methods and articulating trade-offs to stakeholders
Collaborate closely with Trading, Sales, Compliance, Treasury, and Operations to ensure risk analysis and tools are embedded in business decisions
Monitor industry trends, regulatory developments, and emerging best practices in quantitative risk management
Translate complex quantitative concepts into clear, actionable insights for technical and non-technical audiences
Mentor junior team members on quantitative methods, analytics tooling, and professional development
Requirements
8+ years of experience in quantitative finance, financial risk management, or a related quantitative discipline
Advanced degree (Master's or PhD) in a quantitative field
Proven track record of building quantitative analysis tools for risk management (credit, market, liquidity risk)
Deep expertise in risk monitoring and reporting, including experience building or operating real-time risk dashboards and producing executive-level risk reports
Experience with portfolio stress testing and scenario analysis in a professional setting
Experience working with large datasets and analytical tools to support risk analysis
Experience with regulators and regulatory exams
Nice-to-Haves
Experience with digital assets, crypto, or blockchain-related financial products
Understanding of operational risk and how it intersects with credit, market, and liquidity risk
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