Blend360 is an AI-centric consulting firm that helps organizations solve complex business challenges through data, analytics, cloud, and digital transformation. We partner with leading Fortune 500 companies to deliver innovative solutions across industries including financial services, healthcare, retail, consumer goods, and technology. Our collaborative, people-first culture empowers talented professionals to make a meaningful impact while working with cutting-edge technologies and some of the world's most recognized brands.
Job DescriptionOverview
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.
Skills Required
- Hands-on experience in fraud analytics within payments (credit cards, banking, fintech, or 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.
- Bachelor's degree in Mathematics, Statistics, Economics, Computer Science, Data Analytics, Finance, or another quantitative field.
- Experience with fraud 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 for technical and non-technical audiences.
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
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