Data & AI strategy
Identify high-value opportunities, assess readiness, and establish a practical path from data to measurable impact.
I help ambitious teams turn data, analytics, machine learning, and Generative AI into dependable products and better decisions.
From the first data and architecture decision to the production operating model, I focus on the work that makes insight and applied AI trustworthy, useful, and repeatable.
Identify high-value opportunities, assess readiness, and establish a practical path from data to measurable impact.
Design reliable data foundations, analytics workflows, and decision systems that teams can trust and operate.
Move responsibly from machine learning and GenAI exploration to applications that fit real workflows, constraints, and users.
My work sits where data, analytics, engineering, and commercial reality meet — helping teams build capabilities that are not only technically sound, but useful in operation.
Nine years across cloud, mobility, and travel, helping teams apply data, analytics, and machine learning where the stakes are real.
Enabled more than 10 enterprise customers across Southeast Asia to adopt Google Cloud machine learning technologies, including Generative AI.
Designed scalable architectures and MLOps operating patterns on Vertex AI to reduce overhead and cost while increasing development velocity.
Optimized a two-sided mobility marketplace through driver incentive modeling and real-time supply positioning.
Built experimentation-backed optimization systems that saved millions in incentive spend and improved completed orders.
Modernized marketing data pipelines with Spark and Redshift, turning days of processing and report generation into hours.
Developed and scaled experimentation and ranking models for insurance, flight pricing, and hotel recommendations.
Strong technical range is useful when it stays in service of clarity: the right problem, the right system, and a team able to operate it.
Vertex AI, PyTorch, scikit-learn, XGBoost, Generative AI, Bayesian inference, recommender systems
BigQuery, Spark, Redshift, Airflow, Prefect, Kubeflow, Looker, MongoDB
Google Cloud Platform, AWS, Docker, Git, GitHub Actions, GitLab CI
Python, Go, Java, R, Matlab, C
KTH Royal Institute of Technology · 2013–2015
Universitas Indonesia · 2007–2011
Tell me what your team is trying to achieve, where the uncertainty is, and what needs to work in production.