Credit Risk Analyst

Join Coinbase as a Credit Risk Analyst to manage credit exposure and underwriting for institutional clients in the fast-evolving digital asset market. Remote position with a salary range of $60K-$100K. At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency; it’s a place to be pushed past your perceived limits.

• •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.

• •Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)).
• •Annual base salary range (excluding equity and bonus): $95,030 — $111,800 USD.

• •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.