Fraud Data Scientist

Straive; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Straive in Bengaluru, India, is seeking a Fraud Analytics Specialist to identify patterns and anomalies in large datasets. You will build and deploy ML models to detect fraud and generate actionable reports for risk teams.

You will collaborate with risk, operations, and compliance to enhance prevention measures, monitor industry trends, and implement data-driven fraud controls. 3+ years in Python/SQL/ML is expected.

Qualifications

• 3+ years of experience with Python, SQL and ML.
• Hands-on experience with XGBoost and Random Forest.
• Experience building and deploying ML models for fraud detection.
• Ability to analyze large datasets to identify fraud patterns and anomalies.

Responsibilities

• Identify patterns, trends, and anomalies indicating fraudulent activity.
• Develop fraud rules and reports on fraud trends (including first-party and third-party fraud).
• Collaborate with risk management, operations, and compliance to enhance fraud prevention measures.
• Monitor industry trends, regulatory changes, and best practices to improve fraud strategies.

Skills

Python

SQL

Machine Learning

Fraud Detection

Tools

XGBoost

Random Forest

Job description

• Work with large datasets to identify patterns, trends, and anomalies that may indicate fraudulent activity
• Utilize data analytics tools and methodologies to conduct in-depth assessments and generate Fraud rules and reports on fraud trends (including first-party and third-party fraud).
• Collaborate with cross-functional teams, including risk management, operations, and compliance, to enhance fraud prevention measures.
• Monitor industry trends, regulatory changes, and best practices to continually enhance fraud prevention strategies.

Technical Skills Needed:

• 3+ years of experience in Python coding, SQL, Machine Learning
• Hands-on experience with XGBoost, Random Forest,
• Build and deploy ML models for fraud detection.
• Analyze large datasets to identify fraud patterns and anomalies.
• Collaborate with cross-functional teams to enhance fraud prevention strategies.