Data Analyst - Fraud Intelligence

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 requirements and ensure test environments reflect production-like conditions

Document evaluation results and maintain an internal knowledge base on vendor data performance over time

Support ad hoc deep dives into fraud trends, model performance, and client-specific data questions as needed

What you’ll need

3–5 years of experience in data analysis, data science, or a related analytical role — ideally in fraud, risk, fintech, or a data-heavy B2B SaaS environment

Proficiency in SQL (required) and Python or R for data manipulation, statistical analysis, and visualization

Solid understanding of evaluation metrics and statistical concepts: precision/recall, AUC/ROC, lift, population distributions, and A/B testing basics

Experience working with external or third-party datasets — assessing data quality, match rates, and signal value

Strong written and verbal communication skills; ability to synthesize complex analysis into clear narratives for non-technical stakeholders

Comfort with ambiguity and the ability to define your own structure in a fast-moving environment

Bonus Points

Familiarity with fraud signals and data types: device fingerprinting, identity graph data, consortium data, behavioral signals, email/phone intelligence

Experience in a vendor evaluation, data partnerships, or procurement-adjacent analytical role

Exposure to machine learning concepts and feature engineering, even if not in a full ML engineering capacity

Experience working across fintech verticals such as crypto, BNPL, neobanks, or payments

Generous compensation in cash and equity

Early exercise for all options, including pre-vested

Work from anywhere: Remote-first Culture

Flexible paid time off and Year-end break

Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific

4% matching in 401k / RRSP - US and Canada specific

MacBook Pro delivered to your door

One-time stipend to set up a home office — desk, chair, screen, etc.

Monthly meal stipend

Monthly social meet-up stipend

Annual health and wellness stipend

Annual Learning stipend

Join a fast-growing company with world-class professionals from around the world. If you are seeking a meaningful career, you found the right place, and we would love to hear from you.

To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice .