Data Analyst - Fraud Intelligence

About the job YOU will join the Fraud Intelligence team to evaluate vendor data signals and partnerships You will build testing frameworks to translate raw vendor data into actionable fraud intelligence You will collaborate with fraud leadership to define evaluation criteria tied to real fraud outcomes false positive rates catch rates precision recall tradeoffs You will translate vendor data findings into clear recommendations adopt pilot deprioritize or decline You will work with data engineering to define ingestion requirements and ensure test environments reflect production like conditions You will document evaluation results and maintain an internal knowledge base on vendor data performance over time You will support ad hoc deep dives into fraud trends model performance and client specific data questions as needed 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 the 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 Translate vendor data findings into clear actionable recommendations 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 Requirements 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 and Python or R for data manipulation statistical analysis and visualization Solid understanding of evaluation metrics and statistical concepts including precision recall AUC ROC lift population distributions and AB 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