Fraud Data Analyst

Sezzle · Argentina, RemoteRemotomid

En corto

▸Analista de datos especializado en fraude financiero en una fintech.
▸Día a día: investigar transacciones sospechosas, analizar grandes volúmenes de datos con SQL y Python, y crear reglas de prevención de fraude.
▸Destacado: trabaja en una empresa que está redefiniendo la experiencia de compra con planes de pago sin intereses.

Due to the requirements of the position, only English resumes will be accepted.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Título universitario en Economía, Ciencias de la Computación, Análisis o campo relacionado.
✓Mínimo 2 años de experiencia en detección, prevención o investigación de fraude.
✓Mínimo 2 años de experiencia con SQL.
✓Mínimo 2 años de experiencia con Python.
✓Capacidad para investigar transacciones sospechosas y documentar hallazgos.
✓Inglés para el envío de currículum (solo se aceptan CVs en inglés).

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

SQLPythonfraud detection toolsfraud prevention strategiesdata analysistransaction monitoringanomaly detectionfraud trend analysisrisk controlsindustry best practices

¿A quién escribirle en Sezzle?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 detail-oriented and analytical Fraud Data Analyst to join our growing team. The ideal candidate will have a passion for uncovering fraudulent activities and a strong understanding of fraud prevention strategies. In this role, you will be responsible for identifying, investigating, and preventing fraudulent transactions and activities, utilizing your analytical skills and experience with SQL to query and analyze data. You will play a crucial role in protecting our company and our customers from financial loss and reputational damage. Due to the requirements of the position, only English resumes will be accepted. What You'll Do: • Monitor transactions and customer accounts to identify and investigate suspicious or fraudulent activity. • Utilize various fraud detection tools, systems, and techniques to identify emerging fraud trends and patterns. • Conduct in-depth investigations into suspected fraudulent activities, gathering evidence and documenting findings. • Analyze large datasets using SQL to identify anomalies, patterns, and insights related to fraudulent behavior. • Develop and implement fraud prevention rules, strategies, and controls. • Stay up to date with industry best practices, emerging fraud threats, and new technologies in fraud detection and prevention. What We Look For: • Bachelor's degree in a relevant field (e.g. Economics, Computer Science, Analytics, or a related field). • Minimum of two (2) years of experience in a fraud detection, prevention, or investigation role. • Minimum of two (2) years of experience working with SQL and Python

No apliques sin prepararte

Investigamos quién te entrevista, adaptamos tu CV y te ensayamos en vivo — gratis la primera.