job description
Join Monee, a cutting-edge fintech company revolutionizing credit risk systems through advanced machine learning and data infrastructure. We're seeking a Senior Machine Learning Engineer to build and optimize our risk data platforms, driving smarter lending decisions and financial inclusion across Southeast Asia.
In this role, you'll work at the intersection of AI, big data, and financial risk, developing scalable ML models that power our credit scoring, fraud detection, and portfolio management systems. You'll collaborate with cross-functional teams of data scientists, engineers, and product managers to transform raw data into actionable insights that directly impact our business growth and customer experience.
As part of our remote-first team in Bali, you'll enjoy the flexibility of working from one of the world's most vibrant tech hubs while contributing to a mission-driven company. We offer competitive compensation, professional development opportunities, and a culture that values innovation, ownership, and work-life balance.
If you're passionate about applied machine learning in fintech and want to build systems that make a real difference in people's financial lives, we'd love to hear from you. Help us shape the future of credit risk technology in emerging markets!
Responsibility
- Design, develop, and deploy scalable machine learning models for credit risk assessment, fraud detection, and portfolio optimization
- Build and maintain data pipelines and feature stores that feed our risk decisioning systems with high-quality, real-time data
- Collaborate with data scientists to productionize ML models and ensure they meet performance, latency, and reliability requirements
- Implement monitoring and alerting systems to track model performance and data drift in production environments
- Optimize existing ML infrastructure for cost efficiency and scalability, leveraging cloud technologies and distributed computing
- Work with product teams to translate business requirements into technical specifications for risk data solutions
- Conduct A/B testing and experimentation to continuously improve our risk models and decisioning systems
- Mentor junior engineers and contribute to best practices in ML engineering and risk data management
Qualifications
- 5+ years of experience in machine learning engineering, with at least 2 years focused on financial risk or credit systems
- Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, or scikit-learn)
- Experience with big data technologies (Spark, Hadoop, or similar) and cloud platforms (AWS, GCP, or Azure)
- Solid understanding of credit risk modeling, including PD/LGD/EAD concepts and regulatory requirements
- Hands-on experience with MLOps tools (MLflow, Kubeflow, or similar) and CI/CD pipelines
- Familiarity with feature engineering and data preprocessing techniques for structured and unstructured data
- Strong SQL skills and experience working with large-scale databases
- Excellent problem-solving skills and ability to communicate complex technical concepts to non-technical stakeholders
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field