I’m someone who learned early that numbers don’t fail—processes do.
My work has lived close to real decisions. At Allianz Life Indonesia, I handle high-volume, high-stakes financial data where errors have consequences, not just corrections. I reconcile inconsistencies, trace anomalies back to their source, and make sure what reaches stakeholders is something they can rely on. Accuracy isn’t a preference in my work; it’s a baseline.
Before that, I led data functions in student and non-profit organizations, building structure where none existed—cleaning pipelines, defining metrics, and turning scattered inputs into information leaders could actually use. That experience taught me how data behaves outside textbooks: messy, political, and valuable only when translated clearly.
Academically, I trained in Data Science and Business Analytics and graduated with First Class Honours, but what differentiates me isn’t the tools I use—it’s how I think. I question assumptions, sanity-check results, and focus on outputs that stand on their own, whether that’s a model, a dashboard, or a written recommendation.
I stand out because I don’t chase sophistication for its own sake. I care about clarity, trust, and impact—and I’m comfortable taking ownership when the work is ambiguous and the standard is high.
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