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Ridha Rahmat

@ridharahmat

I build forecasting, statistical, and geospatial models that turn large datasets into decisions.

Indonesia
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I've built forecasting, statistical, and spatial models for BMKG, Monash University research teams, and government stakeholders. At BMKG, my time-series forecasting model was adopted into Indonesia's national early-warning and decision-support systems.

I bring six years of experience analysing large operational datasets, including clustering nationwide data into 699 operational forecast zones, producing decision-ready reports and dashboards, and maintaining rigorous data extraction, validation, and quality assurance.

At ARC Centre of Excellence for 21st Century Weather, I engineered a reproducible high-performance computing pipeline for approximately 1.2 TB of time-series data and developed anomaly-detection workflows. At SoDa Labs, I built a Python and GeoPandas pipeline that integrated multiple datasets into a harmonised 30-year regional time series.

I'm completing a Master of Data Science at Monash University and work across Python, SQL, R, machine learning, BI reporting, and geospatial analysis. I enjoy building reusable data products, from churn models and interactive dashboards to PostgreSQL/PostGIS community-data platforms.

Experience

Work history, roles, and key accomplishments

MU

Research Assistant

Monash University

Dec 2025 - Feb 2026 (2 months)

Engineered a reproducible pipeline processing ~1.2 TB of time-series data on high-performance computing, analyzing patterns across regions and time. Developed anomaly detection and built documented, reusable analysis workflows.

Education

Degrees, certifications, and relevant coursework

MU

Monash University

Master of Data Science, Data Science

2024 -

Grade: WAM 82.56/100

Pursuing a Master of Data Science with a WAM of 82.56/100 (High Distinction).

UP

Universitas Padjadjaran

Bachelor of Science, Statistics

2012 - 2016

Grade: GPA 3.70/4.00

Graduated with a Bachelor of Science in Statistics, achieving a GPA of 3.70/4.00 and graduating Cum Laude.

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