Fraud Operations Analyst at Sezzle

About the Role:

Sezzle is seeking a Fraud Operations Analyst to support fraud monitoring. 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:

Monday - Friday between 3:00 PM - 11:30 PM Central Time.

This will be a non-exempt/hourly position.

Compensation:

$2,000 USD per month.

Our Company

Sezzle is a cutting-edge fintech company whose long-standing mission is to financially empower the next generation. Only one in three millennials own a credit card, and the vast majority of millennials possess a subprime credit score or no score at all. To address these problems, Sezzle has built a payment platform that increases purchasing power for consumers by offering interest-free installment plans at online stores. This increase in purchasing power for consumers leads to increased sales and basket sizes for the numerous eCommerce merchants that currently work with Sezzle.

As the first and only B-Corp in the payments space, Sezzle’s goal is to help everyone pursue financial freedom, and to invest in social good and ethical initiatives that shape a better world.

At Sezzle, we’re committed to putting purpose alongside profitability. We work hard to maintain rigorous standards of social and environmental performance, accountability, and transparency to build a better world. The employees at Sezzle lean into significant societal and cultural shifts, ensuring internal and external focus on positive impacts that benefit our community.

Responsibilities:

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.

Ideal Skills & Experience:

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
Minimum GPA of 3.5 out of 4.0
Demonstrated experience working with Claude or equivalent large language model tools is required; candidates must be comfortable leveraging AI to enhance productivity, research, and communication.

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