job description
Monee is a leading digital payments and financial services provider revolutionizing financial inclusion across Southeast Asia and Latin America. As we expand our cutting-edge fintech solutions, we’re seeking a Senior Machine Learning Engineer to join our Risk Data team in Bali, Indonesia. In this role, you’ll develop and deploy advanced ML models to mitigate fraud, assess credit risk, and enhance transaction security—empowering millions with safer, smarter financial services.
Based in Bali’s vibrant tech hubs (Canggu, Ubud, or Denpasar), you’ll collaborate with cross-functional teams to design scalable ML systems, optimize real-time risk detection, and drive data-driven decisions. Your work will directly impact our mission to democratize financial access while maintaining the highest security standards.
If you’re passionate about AI-driven risk management and thrive in a dynamic, innovative environment, this is your chance to shape the future of fintech in emerging markets.
Responsibility
- Design, develop, and deploy machine learning models for fraud detection, credit scoring, and transaction risk assessment.
- Optimize real-time risk decision engines to minimize false positives while maximizing fraud prevention.
- Collaborate with data scientists and engineers to scale ML pipelines for high-volume transaction processing.
- Implement feature engineering and model validation techniques to improve accuracy and performance.
- Monitor and maintain ML model health, including A/B testing, drift detection, and retraining.
- Work with product teams to integrate risk models into core payment and lending platforms.
- Research and prototype novel approaches (e.g., deep learning, graph networks) for emerging risk challenges.
- Ensure compliance with regulatory standards (e.g., GDPR, PCI-DSS) in all model deployments.
Qualifications
- 5+ years of experience in machine learning, with a focus on risk, fraud, or financial services.
- Proficiency in Python, TensorFlow/PyTorch, Scikit-learn, and distributed computing (Spark, Dask).
- Strong background in statistical modeling, supervised/unsupervised learning, and time-series analysis.
- Experience with big data tools (e.g., Kafka, Hadoop, Snowflake) and cloud platforms (AWS, GCP).
- Familiarity with risk management frameworks (e.g., FICO scores, AML systems) and financial regulations.
- Proven ability to productionize ML models (APIs, microservices) and monitor their performance.
- Excellent problem-solving skills and a data-driven mindset for hypothesis testing.
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.