About the role
Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. Location: Remote - United States or Canada From Home / Beach / Mountain / Cafe / Anywhere! We are a remote-first company with a globally distributed team. You can find your productive zone and work from there.
About
The Role We’re looking for a Data Analyst to join Sardine’s Fraud Intelligence team. This role sits at the intersection of data evaluation, vendor strategy, and fraud detection. You’ll be the analytical engine behind how we assess, test, and onboard new third-party data signals and vendor partnerships — determining which data assets actually move the needle on fraud outcomes for our clients. This is a high-ownership, high-visibility role. You’ll work closely with the Head of Fraud, product, data engineering, and client-facing teams to build rigorous testing frameworks and translate raw vendor data into actionable fraud intelligence. What you’ll be doing: Design and execute structured evaluation frameworks to assess the quality, coverage, and fraud-signal value of incoming data assets from vendor partners Build lift analyses, backtests, and champion/challenger comparisons to quantify the incremental value of new data signals against our existing fraud detection stack Profile vendor datasets for completeness, freshness, match rates, and population coverage across verticals (crypto, fintech, neobanks, e-commerce, etc.) Collaborate with fraud leadership to define evaluation criteria tied to real fraud outcomes — false positive rates, catch rates, precision/recall tradeoffs Translate vendor data findings into clear, actionable recommendations: adopt, pilot, deprioritize, or decline Partner with data engineering to define ingestion
