Senior Machine Learning Engineer

About Amartha

At Amartha, we empower micro-businesses across Indonesia, enabling growth and equal prosperity. We've supported over 3.5+ million enterpreneurs–mostly women–by disbursing 2 billion USD in funding. As we step into 2026, Amartha is evolving into a technology-driven financial ecosystem, expanding our reach in lending, funding, and payments. Through innovation and digital solutions, we aim to enhance accessibility, streamline processes, and create a seamless user experience.

Roles and Responsibilities:

• Design, develop, and productionize ML models for credit scoring, underwriting, fraud detection, collections, and portfolio risk management.
• Build robust features from customer, transaction, repayment, behavioral, and alternative data.
• Select and evaluate appropriate algorithms, with emphasis on explainable and high-performing models such as XGBoost.
• Define offline and online evaluation metrics aligned with lending outcomes and business objectives.
• Address class imbalance, data leakage, bias, model stability, and changing customer behavior.
• Build reliable training, validation, deployment, monitoring, and retraining pipelines.
• Monitor model performance, calibration, drift, fairness, and operational impact in production.
• Produce clear model documentation and explain decisions to risk, product, engineering, compliance, and business stakeholders.
• Collaborate with data engineers and software engineers to integrate models into scalable production systems.
• Conduct experiments and translate model improvements into measurable business outcomes.
• Explore practical LLM and agentic-AI applications, such as document processing, underwriting assistance, investigation workflows, and internal productivity tools.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience.
• 5+ years of ML Engineering or Data Scientist experience
• Strong foundation in traditional machine learning, including classification, regression, feature engineering, model evaluation, and imbalanced-data handling.
• Hands-on experience with models such as XGBoost, LightGBM, random forests, and logistic regression.
• Proficiency in Python, SQL, and ML libraries such as scikit-learn, XGBoost, pandas, and NumPy.
• Experience deploying, monitoring, and maintaining ML models in production.
• Understanding of model explainability, drift detection, experiment tracking, and reproducible ML workflows.
• Knowledge of credit risk, underwriting, fraud detection, customer scoring, or related financial-services use cases.
• Experience in fintech, digital lending, or microfinance is strongly preferred.
• Familiarity with cloud platforms, containers, APIs, and data pipelines.
• Exposure to LLMs, retrieval-augmented generation, prompt engineering, or agentic programming is a plus.