Senior Machine Learning Engineer

Аналитика рынка

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Описание

About Amartha At Amartha, we empower micro-businesses across Indonesia, enabling growth and equal prosperity. 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. 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,

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