Data Scientist [Talent Pool all level]

PT. Indosat Tbk; Jakarta Utara; On-site; IDR 300.000.000 - 400.000.000; Full time

Job summary

A leading telecommunications company in Indonesia is looking for a Data Scientist to develop and operationalize AI and machine learning solutions. The role involves transforming business challenges into analytical use cases and managing the full AI lifecycle. Candidates should have 3-6 years of experience in machine learning, strong proficiency in Python and SQL, and experience with cloud ML platforms. The position offers an exciting opportunity to lead impactful AI initiatives.

Qualifications

• 3–6 years of experience in machine learning model development and experimentation, ideally in telecom, fintech, or technology sectors.
• Understanding of Responsible AI, bias testing, and model explainability principles.
• Strong communication and documentation skills for ARS and Model Card preparation.

Responsibilities

• Translate business needs into technical requirements and define model objectives, input data, and success metrics.
• Conduct exploratory data analysis (EDA), 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 business signals.
• Build, test, and maintain Vertex AI pipelines from UDP (Unified Data Platform) to ACE (AI CoE Environment).
• Maintain up-to-date model documentation including data lineage, assumptions, KPIs, and Responsible AI checks.

Skills

Python

SQL

Machine Learning frameworks (TensorFlow, PyTorch, Scikit-learn)

Cloud ML platforms (Vertex AI, AWS SageMaker, Azure ML)

Communication skills

Education

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

Job description

The Data Scientist plays a pivotal role in developing and operationalizing AI and machine learning solutions that address key business challenges across consumer, enterprise, and corporate domains. This role transforms complex business questions into analytical use cases, designs and trains predictive and prescriptive models, and ensures each model delivers measurable business outcomes such as churn reduction, ARPU growth, cost optimization, and customer satisfaction improvement. Beyond model creation, the Data Scientist leads the full AI lifecycle — from data exploration, feature engineering, and model development to testing, pipeline deployment, and continuous performance tracking. Each model is documented through Model Cards and deployed using Vertex AI pipelines (UDP to ACE) to ensure scalability, transparency, and compliance.

By combining deep analytical expertise with Responsible AI practices, the Data Scientist ensures that every solution contributes to embedding AI as a strategic, value-generating capability across the organization

Responsibilities

• Translate business needs into technical requirements and define model objectives, input data, and success metrics.
• Conduct exploratory data analysis (EDA), 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 business signals.
• Build, test, and maintain Vertex AI pipelines from UDP (Unified Data Platform) to ACE (AI CoE Environment).
• Maintain up-to-date model documentation including data lineage, assumptions, KPIs, and Responsible AI checks.

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, ideally in telecom, fintech, or technology sectors.
• Strong proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience working with cloud ML platforms (Vertex AI, AWS SageMaker, or Azure ML) and pipeline orchestration.
• Understanding of Responsible AI, bias testing, and model explainability principles.
• Strong communication and documentation skills for ARS and Model Card preparation.