Senior Fraud Data Scientist — Real-Time Risk & ML

YTL Sea Digital Bank Project; Kuala Lumpur; On-site; MYR 180,000 - 280,000; Full time

Job summary

Ryt Bank in Malaysia is seeking a Senior Data Scientist to strengthen fraud detection across the customer lifecycle. You will work with large datasets, real-time signals, and evolving attack patterns to design transparent, measurable strategies that balance risk and customer experience.

The role requires building and validating ML models and rules, analyzing patterns including synthetic identities and deepfakes, and collaborating with product, engineering, and fraud ops to deploy real-time

Qualifications

• 2-5+ years of experience in fraud analytics, data science, or risk modelling in digital banking or payments.

Responsibilities

• Build and maintain ML models and rule-based systems to detect fraud across loans, payments, cards, and related activities.
• Develop and run backtesting and simulations to validate rules and models and minimize false positives.
• Combine signals into explainable detection strategies and monitor performance against KPIs.
• Analyze behaviour, transaction patterns, and docs to identify new fraud typologies including synthetic identities and manipulated documents.
• Collaborate with product, engineering, and fraud operations to deploy real-time detection strategies.
• Tune model thresholds, features, and decision logic as attack vectors evolve.
• Design experiments to measure impact on approval rates, fraud loss, and user experience.
• Build dashboards, reporting pipelines, and alerts to track fraud KPIs across products.
• Stay ahead of emerging fraud techniques in digital lending, payments, and identity creation.

Skills

Python

SQL

Fraud analytics

Risk modeling

Machine learning

Tools

XGBoost

PySpark

Job description

Ryt Bank in Malaysia is seeking a Senior Data Scientist to strengthen fraud detection across the customer lifecycle. You will work with large datasets, real-time signals, and evolving attack patterns to design transparent, measurable strategies that balance risk and customer experience.

The role requires building and validating ML models and rules, analyzing patterns including synthetic identities and deepfakes, and collaborating with product, engineering, and fraud ops to deploy real-time