gilbran — profl.id

Profile

About

Transitioning into a data analysis role built on integrity, a strong work ethic, and reliable, well-reasoned decision-making. Background spans freelance data-entry work, digital marketing performance for advertising clients, and ongoing management of a family property business. Through an intensive data analytics bootcamp, sharpened technical skills in SQL, spreadsheets, Python, and Tableau — paired with a detail-oriented, stakeholder-focused approach to communicating insight.

Background

Education

Formal engineering training combined with focused, applied data-analytics training.

Aug 2020 — Jul 2024

Universitas Katolik Parahyangan

B.Eng. in Industrial Engineering · GPA 3.33

Feb 2026 — Jun 2026

RevoU

Full Stack Data Analyst Program

End-to-end applied training in data analysis — from framing business problems and processing data to building visualizations with Spreadsheets, SQL, Python, Power BI, and Tableau.

Track record

Work Experience

Roles that built a foundation in data handling, accuracy under volume, and performance-driven decision-making.

Sep 2024 — Jan 2025

Data Entry — Remote

Holme and Hadfield

Performed comprehensive data entry — researching, verifying, and organizing large datasets for marketing, investor relations, and customer order records to support fulfillment.
Processed and analyzed 5,000+ customer and investor records to inform growth planning and engagement strategy.
Maintained 1,000+ customer order records, resolving shipping issues to ensure accurate tracking and order fulfillment.

May 2025 — Dec 2025

Advertiser

Marketlab Indonesia

Managed advertising strategy for 6+ clients across TikTok Ads Manager, Meta Ads, and Shopee Ads to drive measurable revenue and traffic growth.
Built ad strategies across Shopee Ads, TikTok Ads Manager, and Meta Ads that delivered an average 70% month-over-month revenue increase alongside traffic growth.
Rolled out an initial digital marketing strategy for a new client, driving 148% revenue growth and 100% traffic growth within two months.

Portfolio

Project Experience

Applied analytics work — from raw data to a model with a measurable operational recommendation.

Supply Chain Delivery Performance Analysis

Apr 2026 — Jun 2026

DataCo Global

Core stack Python Tableau Spreadsheets

Cleaned and analyzed a 79,107-row logistics dataset in Python (pandas, geopy, scikit-learn/XGBoost) — engineering shipping-distance and processing-time features to isolate supply chain bottlenecks behind a 57.27% late-delivery rate. Compared Logistic Regression, Random Forest, and XGBoost to predict at-risk shipments, then built an interactive Tableau dashboard to surface late-rate drivers by shipping mode, region, and profit trend for stakeholders.

79,107Rows Processed

57.27%Baseline Late Rate

70.65%XGBoost Accuracy

0.772ROC-AUC

54.6%Est. Late-Delivery Reduction

Tableau dashboard showing total profit, late delivery rate, late rate by shipping mode, profit trend, and a geographic map of late rate by region — Tableau dashboard — live filters for order status, date, region, and shipping mode; tracks late rate, profit, and geographic risk in one view.

Findings pointed to shipping SLA as the strongest driver of delays — the "First Class" mode carried a 100% late rate — with destination region as a secondary factor. Built as a risk-scoring model, XGBoost's output could flag high-risk orders before dispatch, projecting the late rate down from 57.27% to roughly 25.6%.

Python notebook → Tableau dashboard → Spreadsheet workbook →

Gilbran Dharmawangsa