Sr. Quantitative Risk Manager - Liquidity Finance & Accounting

About the Role

Join Kraken's finance and risk team to lead the quantitative modeling and analysis for liquidity risk management across our global operations. This is a critical role ensuring the financial stability and regulatory compliance of our rapidly growing digital asset exchange.

Responsibilities

• Develop, implement, and validate quantitative models for measuring and forecasting liquidity risk across various asset classes and jurisdictions.
• Design and maintain stress testing and scenario analysis frameworks specifically tailored to crypto-asset market dynamics.
• Collaborate closely with Finance, Treasury, and Accounting teams to ensure risk models align with regulatory reporting requirements (e.g., internal liquidity risk frameworks).
• Produce high-quality documentation for models, methodologies, and results for internal review and potential external regulatory scrutiny.
• Monitor the performance of existing liquidity risk models and recommend improvements or recalibrations as market conditions evolve.

Requirements

• Master's degree or PhD in a quantitative field such as Financial Engineering, Mathematics, Statistics, Physics, or Computer Science.
• Minimum of 5 years of professional experience in quantitative risk management, preferably within banking, fintech, or capital markets, focusing on market or liquidity risk.
• Deep understanding of financial instruments, derivatives, and the unique characteristics of digital assets (cryptocurrencies).
• Proven expertise in statistical modeling, time-series analysis, and programming languages essential for quantitative analysis (e.g., Python/R).
• Excellent communication skills with the ability to explain complex quantitative concepts to non-technical stakeholders.

Nice-to-Haves

• Experience working in a regulated environment or familiarity with global financial regulations pertaining to liquidity risk (e.g., LCR, NSFR concepts).
• Prior experience building or validating models specifically for non-traditional or volatile asset classes.
• Familiarity with cloud computing environments (AWS, GCP) and big data tools.