Senior Machine Learning Engineer

📋 Job Overview

At Amartha, we empower micro-businesses across Indonesia, enabling growth and equal prosperity. We've supported over 3.5+ million entrepreneurs–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.

🏢 About Amartha

Amartha is a financial technology company focused on empowering micro-businesses in Indonesia. With a mission to enable growth and equal prosperity, we have supported over 3.5+ million entrepreneurs, primarily women, by providing 2 billion USD in funding. Our vision for 2026 includes transforming into a technology-driven financial ecosystem that expands our reach in lending, funding, and payments.

🎯 The Role

As a Machine Learning Engineer at Amartha, you will be responsible for designing, developing, and productionizing ML models for credit scoring, underwriting, fraud detection, collections, and portfolio risk management. You will work with diverse data sources, including customer, transaction, repayment, behavioral, and alternative data, to build robust features and select appropriate algorithms.

✅ Key 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• Exposure to LLMs, retrieval-augmented generation, prompt engineering, or agentic programming is a plus.

🎁 Benefits

Competitive salary and benefits package.

🛂 Visa & Eligibility

No specific visa or eligibility information provided.