About AmarthaAt 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.RequirementsBachelor’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 experienceStrong 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.At Amartha, we are dedicated to creating a workplace that celebrates diversity, ensures equity, and fosters inclusion. We believe that diverse perspectives—shaped by factors such as gender, age, race, ethnicity, education, culture, and life experiences—drive innovation and growth.We actively welcome individuals from all backgrounds to join us in building an environment where everyone feels respected, valued, and empowered. Our commitment is to provide equal opportunities and foster a sense of belonging that enables our employees to thrive and make meaningful contributions.
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.
At Amartha, we are dedicated to creating a workplace that celebrates diversity, ensures equity, and fosters inclusion. We believe that diverse perspectives—shaped by factors such as gender, age, race, ethnicity, education, culture, and life experiences—drive innovation and growth.
We actively welcome individuals from all backgrounds to join us in building an environment where everyone feels respected, valued, and empowered. Our commitment is to provide equal opportunities and foster a sense of belonging that enables our employees to thrive and make meaningful contributions.
