Credit Risk Analyst

We're hiring a Credit Risk Analyst to join the Counterparty and Credit Risk team within Financial & Operational Risk. This team manages credit exposure across Coinbase's global institutional client base, underwriting counterparties and delivering lending solutions in a fast-evolving digital asset market. You'll own credit analysis for complex counterparties, recommend risk-based limits, and monitor portfolio exposures that directly inform how Coinbase scales its institutional products. This role is based in the United States or United Kingdom.

What you'll do

• Own diligence and underwriting of institutional counterparties including hedge funds, asset managers, private equity, and venture capital firms.
• Recommend internal credit ratings and risk-based limits, preparing written analysis that substantiates each assessment for senior leadership review.
• Monitor portfolio exposures, conduct periodic surveillance of trading partners, and clearly communicate concentration trends and settlement risks to leadership.
• Analyze asset volatility and liquidity risks on a per-asset and portfolio basis to inform limit-setting and capital planning.
• Negotiate industry documentation (e.g., PBAs, MLAs, and ACAs) and collaborate with business development, operations, legal, and product teams to resolve complex client or product challenges.

Required Skills and Experience

• 1-3 years of hands-on credit risk management experience, including counterparty underwriting or portfolio monitoring in a fast-moving financial services or digital asset environment.
• Prior experience covering institutional clients such as hedge funds, asset managers, private equity, or venture capital firms.
• Working knowledge of capital markets products and standard industry documentation (PBAs, MLAs, prime brokerage agreements).
• Demonstrated ability to produce clear written credit memos and present risk recommendations to senior stakeholders.
• Proficiency building quantitative credit models and exposure analyses using spreadsheets, SQL, or other analytical tools.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

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