Data Analyst - Fraud Intelligence
New
S
SardineFintech / Fraud
Remote - United States or CanadaFull-TimeMiddle
SalaryUnited States: $115K – $145K • Offers Equity; Canada: CA$135K – CA$175K • Offers Equity
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Job Details
- Experience
- 3–5 years
- Required Skills
- PythonSQLData AnalysisData engineeringData modelingA/B testingR
Requirements
- 3–5 years of experience in data analysis, data science, or a related analytical role
- Experience in fraud, risk, fintech, or a data-heavy B2B SaaS environment
- Proficiency in SQL
- Proficiency in Python or R for data manipulation, statistical analysis, and visualization
- Solid understanding of evaluation metrics including 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 with ability to synthesize complex analysis for non-technical stakeholders
- Comfort with ambiguity and ability to define structure in a fast-moving environment
Responsibilities
- 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
- Collaborate with fraud leadership to define evaluation criteria tied to real fraud outcomes such as false positive rates and precision/recall
- 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
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