Lead Data Scientist: AI Strategy & Impact

PT. Indosat Tbk; Jakarta Pusat; On-site; IDR 30,000,000 - 65,000,000; Full time

Job summary

PT. Indosat Tbk is seeking a Data Scientist to develop and operationalize AI/ML solutions across consumer, enterprise, and corporate domains. You will transform business questions into analytical use cases, design and train models, and track measurable outcomes such as churn reduction and ARPU growth.

Responsibilities include end-to-end AI lifecycle from data exploration to pipeline deployment, and documenting models with Model Cards. Collaboration with cross-functional teams is essential.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or Applied Mathematics.
• 3–6 years of experience in machine learning model development and experimentation.
• Strong proficiency in Python, SQL, and ML frameworks.
• Experience with cloud ML platforms and pipeline orchestration.
• Understanding of Responsible AI and model explainability.
• Strong communication and documentation skills.

Responsibilities

• Translate business needs into technical requirements and define model objectives, input data, and success metrics.
• Conduct exploratory data analysis, cleansing, transformation, and feature selection.
• Build, train, and test machine learning models aligned to business use cases and Responsible AI standards.
• Engineer new features to improve model performance and capture new signals.
• Maintain Vertex AI pipelines from UDP to ACE and document data lineage and KPIs.

Skills

Python proficiency

SQL proficiency

ML model development

Communication

Responsible AI

Education

Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or Applied Mathematics

Tools

TensorFlow

PyTorch

Scikit-learn

Vertex AI

AWS SageMaker

Azure ML

Job description

PT. Indosat Tbk is seeking a Data Scientist to develop and operationalize AI/ML solutions across consumer, enterprise, and corporate domains. You will transform business questions into analytical use cases, design and train models, and track measurable outcomes such as churn reduction and ARPU growth.

Responsibilities include end-to-end AI lifecycle from data exploration to pipeline deployment, and documenting models with Model Cards. Collaboration with cross-functional teams is essential.