Data Scientist - Domain AI & Model Engineering

PT. Indosat Tbk; Jakarta Pusat; Presencial; IDR 400 000 000 - 650 000 000; Tempo integral

Resumo da oferta

PT. Indosat Tbk is seeking a data science leader to translate business problems into robust, production-ready models and to drive end-to-end data science lifecycle in Jakarta. You will collaborate with data engineering, MLOps, and business owners to industrialize models that create measurable value.

The role emphasizes feature engineering, experimentation, GenAI literacy, and strong Python/SQL capabilities to deliver transportable outputs for decision products and automated interventions.

Qualificações

• Minimum bachelor's degree in STEM, Statistics, Mathematics, Computer Science, Engineering, Economics or another quantitatively rigorous discipline.
• Proven ability to translate business problems into analytical hypotheses, experiments and production-ready models.

Responsabilidades

• Translate priority business problems into reusable model and experimentation roadmaps.
• Identify where predictive, forecasting, optimization, recommendation or GenAI approaches create meaningful business advantage.
• Define analytical success metrics that connect model quality to business outcomes and value.
• Execute the end-to-end Data Science lifecycle: data exploration, feature engineering, modelling, validation, experimentation, deployment support and monitoring.
• Design outputs that downstream decisioning and agentic systems can consume safely, including scores, drivers, confidence, eligibility and action recommendations.
• Continuously monitor drift, model performance, adoption and realized impact; recommend retraining or redesign where needed.
• Work closely with Data Engineering, AI Platform/MLOps, AI BI + Agentic Factory and business owners to industrialize models.
• Contribute reusable methods, features, code and evaluation patterns that reduce one-off Data Science work.
• Support knowledge sharing and raise the technical bar across the Data Science community.
• Evaluate relevant external AI/ML tools, platforms, datasets and partners where they materially improve delivery.
• Represent IOH in selected technical / industry discussions and stay current on applied ML and GenAI developments.
• Avoid unnecessary dependency on external vendors by building reusable internal capability.

Conhecimentos

Python

SQL

Feature Engineering

Data Visualization

Machine Learning

Statistics

Causal Inference

Optimization

Experimentation

Model Deployment

RAG

Prompt Engineering

Git

Formação académica

Bachelor's degree in STEM / quantitative discipline

Ferramentas

Vertex AI

BigQuery

Descrição da oferta de emprego

• Build predictive, prescriptive and generative intelligence that improves business decisions across IOH priority domains, while creating reusable models and analytical assets for downstream decisioning and agentic workflows.
• Translate business problems into analytical hypotheses, experiments and production-ready models across customer, commercial, network and enterprise use cases.
• Develop model outputs beyond a single score, including key drivers, confidence, eligibility, expected value and recommended action where relevant.
• Partner with Data Engineering and AI Platform/MLOps to ensure data readiness, deployment, monitoring, retraining and production reliability.
• Partner with AI BI + Agentic Factory so model outputs can be consumed safely in business workflows, decision products and automated interventions.
• Monitor model performance, drift, adoption and realized business impact while applying responsible AI, fairness and explainability principles.

Strategic Planning

• Translate priority business problems into reusable model and experimentation roadmaps.
• Identify where predictive, forecasting, optimization, recommendation or GenAI approaches create meaningful business advantage.
• Define analytical success metrics that connect model quality to business outcomes and value.

Operational

• Execute the end-to-end Data Science lifecycle: data exploration, feature engineering, modelling, validation, experimentation, deployment support and monitoring.
• Design outputs that downstream decisioning and agentic systems can consume safely, including scores, drivers, confidence, eligibility and action recommendations.
• Continuously monitor drift, model performance, adoption and realized impact; recommend retraining or redesign where needed.

Cross-Functional Delivery

• Work closely with Data Engineering, AI Platform/MLOps, AI BI + Agentic Factory and business owners to industrialize models.
• Contribute reusable methods, features, code and evaluation patterns that reduce one-off Data Science work.
• Support knowledge sharing and raise the technical bar across the Data Science community.

External Relation

• Evaluate relevant external AI/ML tools, platforms, datasets and partners where they materially improve delivery.
• Represent IOH in selected technical / industry discussions and stay current on applied ML and GenAI developments.
• Avoid unnecessary dependency on external vendors by building reusable internal capability.

Qualification:

Minimum bachelor's degree in STEM, Statistics, Mathematics, Computer Science, Engineering, Economics or another quantitatively rigorous discipline.

Experience:

Minimum 4-8 years in applied Data Science, Machine Learning or quantitative analytics, with evidence of solving real business problems and moving models beyond experimentation into operational use.

Skills:

• Strong Python and SQL capability, including feature engineering, reproducible analysis and large-scale data manipulation.
• Strong applied machine learning and statistics fundamentals including classification, regression, forecasting, experimentation and model evaluation.
• Experience with causal inference, optimization, recommendation or next-best-action approaches is highly valued.
• Experience deploying or supporting models on cloud ML platforms such as Vertex AI, including monitoring, retraining and model lifecycle practices.
• Working knowledge of BigQuery / modern cloud data environments and collaboration with data engineering teams.
• GenAI literacy including foundation models, prompt engineering, RAG, structured outputs and model-to-agent consumption patterns.
• Strong business judgment: able to connect model performance to intervention economics, adoption and measurable business value.
• Able to communicate analytical trade-offs, limitations, explainability and responsible-AI considerations clearly to technical and business stakeholders.