Quantitative
Statistics, optimization, experimentation, forecasting
About / professional profile
The work is bigger than the job title.
Hi there, my name is Aryanto, a senior data and AI practitioner whose career has moved through analytics, data engineering, machine learning, computer vision, quantitative modeling and technical leadership. I work at the intersection where a business problem needs both analytical judgment and production-minded engineering.
Aryanto, M.Si
Data · AI · Engineering
Career intersection
One employer, one formal role with many disciplines in the actual work.
At Merkle, my formal role was Data Science Manager. The work, however, crossed customer analytics, recommendation, Customer Data Platform architecture, Learning-to-Rank, Next Best Offer, Multi-Touch Attribution, conversational AI with RAG, and team leadership.
At Asia Pulp & Paper, the formal roles were Data Scientist and Senior Data Scientist, while the projects also demanded optimization, data-system thinking, remote-sensing image analytics and production-oriented machine learning.
Engineering discipline
A stack built around outcomes, not labels.
“ My quantitative foundation in Mathematics and Actuarial Science helps me choose the right tool for the problem: statistics when uncertainty matters, optimization when constraints dominate, ML when prediction helps, GenAI when language workflows benefit, and engineering when the solution must survive production. ”
Data
SQL/noSQL, ETL, data models, cloud platforms, GIS
ML / AI
Predictive ML, recommender systems, NLP, GenAI, RAG
Engineering
Productionization, APIs, CI/CD, MLOps, performance
Career signal
Where the experience accumulated.
Formal role durations are shown separately because some engagements overlapped. The point is not to inflate a single employment clock, but to show how the capability stack developed across different environments.
Career Experience Timeline
Tenure per Role & Company
Y-Axis: Duration (Years)
“ That distinction matters: I am not claiming multiple simultaneous titles inside one company. I am explaining the intersection of tasks. A role can be formally Data Scientist while the work requires Data Engineering, AI Engineering or Computer Vision skills. ”
What I can offer
A senior bridge between business, data and engineering.
- Frame ambiguous business questions into measurable hypotheses.
- Build analytics, ML, GenAI or data-engineering capabilities directly.
- Review architecture, technical trade-offs and production readiness.
- Mentor teams while keeping delivery connected to outcomes.
Failure Modes I Prevent
Silent problems are usually more expensive than visible ones.
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Data drift
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Train-serving skew
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Feedback loops
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Unmeasured ROI
Have a problem that needs more than one discipline?
Tell me the business outcome and constraints. We can determine whether the right answer is analytics, engineering, ML, GenAI, or a combination.
Start the conversation