Senior Fraud & Risk Analyst — Strategy & Automation

Klook; Singapore; Sur place; SGD 90 000 - 150 000; Plein temps

Résumé du poste

Klook, a leading pan-regional experiences platform in Asia Pacific, is hiring a Senior Fraud Analyst to join its Risk Control / Operations team. The role focuses on managing risk strategies for payments, transactions, and user accounts, with a strong emphasis on data-driven decision making.

Ideal candidates have a Bachelor's degree in data science or statistics, 5+ years in credit card fraud prevention, and proficiency in SQL and Python.

Qualifications

• Bachelor's degree or higher in data science, statistics, or related field.
• Over 5 years of experience in credit card fraud prevention and risk control strategies.
• Experience with credit card chargebacks and familiarity with international payment markets.
• Strong data analysis skills; proficient in SQL and Python; ML knowledge a plus.
• Excellent communication and collaboration with product and technical teams.
• Good written and spoken English proficiency.

Responsabilités

• Lead risk control strategies for payments, transactions, and user accounts.
• Analyze risk trends, identify fraud patterns, validate and optimize models.
• Collaborate with data and development teams to build risk systems and decision engines.
• Design, develop, and automate monitoring reports for key risk indicators.
• Investigate black and gray markets to gather risk intelligence and inform decisions.

Connaissances

Data analysis

SQL

Python

Machine learning

Communication

English

Formation

Bachelor's degree in data science, statistics, or related field

Outils

SQL

Python

Data analysis tools

Description du poste

Klook, a leading pan-regional experiences platform in Asia Pacific, is hiring a Senior Fraud Analyst to join its Risk Control / Operations team. The role focuses on managing risk strategies for payments, transactions, and user accounts, with a strong emphasis on data-driven decision making.

Ideal candidates have a Bachelor's degree in data science or statistics, 5+ years in credit card fraud prevention, and proficiency in SQL and Python.