Fraud Operations Analyst

The salary range for this role is $1,500 - $2,000 per month (Gross in USD)

About Sezzle:

With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!

About the Role: 

We are seeking a talented and motivated Fraud Operations Analyst  who is best in class with a high IQ plus a high EQ. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement.

As part of optimizing the online shopping experience, Sezzle constantly monitors users and transactions for abusive or fraudulent behavior. Our Fraud Operations team reviews suspicious activity flagged by Sezzle’s fraud detection system or escalated from Customer Support agents. Fraud Operations Analysts will block users deemed fraudulent or risky, and clear false positive flags from our automated systems and support agents. We monitor for new trends in fraudulent behavior and continuously evolve our policies and procedures to stay one step ahead. We are looking for a teammate who can work one of the shifts listed below full-time:

• Sunday - Thursday between 8:00 am - 4:30 pm (Turkish time) / 12:00 am - 8:30 am - Central Time.
• Tuesday - Saturday between 8:00 am - 4:30 pm (Turkish time) / 12:00 am - 8:30 am - Central Time.

This will be a non-exempt/hourly position.

What You'll Do:

• Monitor real-time transactions and other customer behaviors flagged for fraud and abuse for manual review.
• Investigate individual transactions using internal policies and review methodologies to determine if the transactions are fraudulent or not.
• Understand our systems and tools; investigate account patterns through data analysis.
• Research fraud and user behavior to contribute to machine learning models, rules and other detection systems.
• Collaborate with analysts, operations specialists, data scientists and engineering to improve our fraud prevention mechanisms, processes and tools.
• Learn and maintain strong domain knowledge of the world of fraud including prevention techniques and technologies.
• Maintain or exceed established service level agreements (SLAs) for timely resolution of queued cases to minimize potential losses.
• Handle escalations from internal and external stakeholders in a professional and efficient manner.

What We Look For:

• Bachelor's degree from an accredited institution
• Minimum 1 year of experience in investigating and resolving fraud incidents
• Experienced in analyzing data and comfortable making impactful decisions in a fast-paced and sometimes ambiguous environment
• Attention to detail and ability to multitask
• Excellent problem-solving and analytical skills
• Strong business judgment and communication skills
• Ability to self-start and work with minimal supervision after training
• Able to work through holidays

Preferred Qualifications 

• Work experience in fintech, payments, lending, banking, or financial institutions is a plus
• Degree in statistics, math, finance, economics, or related quantitative discipline; graduate degree a plus
• Experience in using database query language (e.g. SQL, etc), and data processing and analytics tools such as Python / R / SAS is a plus
• Ideal candidate will be available to start within two weeks following an offer
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