FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPython
About the role
Key responsibilities & impact- Design, develop, and deploy machine learning models for fraud and AML detection
- Support batch and real-time transaction scoring scenarios
- Build and maintain MLOps pipelines for model training, validation, deployment, monitoring, and retraining
- Collaborate with data engineers on feature engineering pipelines and maintain the Predator feature dictionary and sync mechanisms
- Optimise model performance to meet latency and TPS targets for real-time fraud decisioning
- Conduct model validation, A/B testing, permutation importance analysis, and champion/challenger evaluations
- Work with the Architecture Review Committee to align ML platform choices with the modernization architecture
- Track advances in fraud detection ML, including graph-based models, anomaly detection, and generative AI applications, and propose relevant adoptions
- Mentor junior team members and contribute to knowledge sharing across squads
Requirements
What you’ll need- 3+ years building and deploying production ML systems in Python
- Working knowledge of cloud-native ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
- Experience with containerisation using Docker and Kubernetes
- Hands-on experience with CI/CD for ML pipelines
- Experience with fraud detection and AML models
- Eligible to work in Malaysia