Blend is seeking a Fraud Analytics Lead Analyst to support fraud strategy and analytics across our financial services clients. In this role, you will leverage data to identify fraud trends, uncover emerging risks, and develop strategies that help prevent fraud across the payments lifecycle.
You will work in a highly collaborative, data-driven environment, partnering with fraud, risk, analytics, and technology teams to improve fraud detection, reduce losses, and strengthen controls against evolving fraud threats.
What You'll Be Doing
- Analyze large datasets to identify fraud trends, patterns, and emerging risks across payment and card portfolios.
- Develop and support fraud risk strategies across the full fraud lifecycle, including application fraud, synthetic identity fraud, and account takeover.
- Translate complex data into actionable insights that drive fraud prevention strategies and business decisions.
- Partner with Fraud Policy, Operations, Risk, and Technology teams to implement, optimize, and enhance fraud decision strategies.
- Support the testing, implementation, monitoring, and continuous improvement of fraud decision systems.
- Create reports, dashboards, and analytical insights to measure strategy performance and identify optimization opportunities.
- Assist with fraud model monitoring, validation, and performance evaluation within production environments.
- Document analyses, findings, and recommendations, and present insights to business partners and senior leadership.
Required Experience
- Hands-on experience in fraud analytics within the payments industry, including credit cards, banking, fintech, or other financial services.
- Strong analytical background working with large, complex datasets in Big Data environments.
- Proficiency with data analysis tools such as SQL, Python, SAS, Hive, or similar technologies.
- Demonstrated ability to identify fraud patterns, trends, anomalies, and actionable insights from complex data.
- Experience supporting business decisions through data analysis, reporting, and strategic recommendations.
Preferred Qualifications
- Experience working with fraud models, including model monitoring, validation, or performance evaluation.
- Familiarity with fraud decision engines, rules-based strategies, or fraud strategy implementation.
- Exposure to risk management, regulatory, or financial services environments.
- Excellent communication and presentation skills, with the ability to explain complex analytical findings to both technical and non-technical stakeholders.
Education
- Bachelor's degree in Mathematics, Statistics, Economics, Computer Science, Data Analytics, Finance, or another quantitative field.
What This Role Requires
This is not a general Data Analyst position. Successful candidates will have direct, hands-on experience in fraud analytics within the payments industry and be comfortable working in large-scale, data-intensive environments. You should have a proven ability to leverage data to identify fraud risks, develop actionable insights, and support strategies that reduce fraud losses while improving the customer experience.
