Data Analyst - Fraud Intelligence

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