Machine Learning Engineer jobs in Indonesia

Indodana Fintech is an OJK-licensed financial technology company that operates a credit marketplace for peer-to-peer loans. Our mission is to achieve financial inclusion by enabling lenders to provide loans to the 100 million underbanked Indonesians. Leveraging sophisticated big data and artificial intelligence technologies, we connect hundreds of lenders with creditworthy borrowers every day.

Our team hailed from Silicon Valley Tech companies such as Google, Microsoft, LinkedIn and Sofi as well as Indonesian startups such as Doku, Touchten. We have graduates from well known universities such as Universitas Indonesia, ITB, Stanford, University of Washington, Cornell and many others. We are building a company with the same culture of openness, transparency, drive and meritocracy as Silicon Valley companies. Join us in our cause to build a world class fintech company in Indonesia.

Job Description

• Lead the design, development, and deployment of machine learning models for risk scoring, fraud detection, and behavioural analytics
• Drive feature engineering, model training, and model monitoring pipelines using large-scale datasets
• Build and maintain a scalable data infrastructure in collaboration with the Data Engineering team
• Manage and mentor a team of data scientists and engineers, ensuring high standards in model accuracy, interpretability, and governance
• Translate complex business problems into data-driven solutions and communicate technical insights clearly to stakeholders
• Lead model validation, backtesting, and performance analysis for new and existing models
• Develop and implement data quality checks, pipelines, and data governance protocols

Qualifications

• Bachelor’s or Master’s in Data Science, Computer Science, Applied Mathematics, or related field
• 3+ years of experience in data science, machine learning, or risk modeling, ideally in fintech or banking
• Strong command of Python, SQL, and big data tools (e.g., Spark, Airflow, Hadoop, AWS/GCP/Databricks)
• Experience leading end-to-end model development in production environments
• Solid understanding of credit risk, fraud analytics, or financial modelling