Join Snapmint as a Data Analyst in our Fraud Risk Team and help drive end-to-end fraud prevention for high-scale digital lending products. In this role, you'll work on real-time fraud detection, rule building, and data analytics to minimize fraud losses while ensuring a seamless customer experience.
Key Responsibilities:
Monitor fraud metrics across products including Checkout, EMI, Pay Later, Personal Loans, and UPI Credit
Identify fraud patterns through deep-dive data analysis
Build, test, and optimize fraud rules within decisioning systems
Collaborate with technology teams to deploy real-time fraud controls
Support fraud model development using internal, bureau, and third-party data
Partner with Fraud Operations to investigate trends and improve fraud detection
Track rule and model performance to reduce false positives while maintaining strong fraud capture.
Requirements - Must-Have
2–5 years of experience in Fraud Risk or Risk Analytics (fintech or digital lending experience preferred)
Strong understanding of the credit lifecycle and underwriting
Proficiency in SQL, Python or R (including libraries such as Pandas and Matplotlib), and Excel
Robust analytical, problem-solving, and data interpretation skills
Experience in fraud rule creation and strategy tuning is an advantage
Hands-on experience with dashboarding tools such as Databricks and Tableau
Requirements - Good to Have
Exposure to fraud tools and rule engines
Experience working with third-party data providers (credit bureaus, device intelligence, etc.)
Basic understanding of machine learning models used in fraud detection
What Makes You a Great Fit
Hands-on approach with a robust sense of ownership
Ability to balance effective fraud prevention with an excellent customer experience
Comfortable working in a fast-paced, high-growth startup workplace