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Data Analyst
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I am a person who is driven by a strong enthusiasm for data analytics. My experiences have enhanced my proficiency in SQL, R, Python, and Tableau. I have successfully created machine learning models for various projects, including Sentiment Analysis, Credit Card Fraud Detection, and reporting channel performance. I am eager to leverage my analytical expertise and explore more potential career opportunities.
RevoU
Certificate in Full-Stacked Data Analytics Programs · Data Analytics
June 1, 2025 – February 1, 2026
Universitas Bina Nusantara
Bachelor of Computer Science and Statistics · Computer Science and Statistics
September 1, 2019 – November 1, 2024
Devin Khosasih
Videographer
February 1, 2025 – August 1, 2025
Jakarta, Jakarta, Indonesia
Binus University
Junior Researcher
September 1, 2023 – February 1, 2024
Jakarta, Jakarta, Indonesia
OCA x RevoU Virtual Intern
December 1, 2025 – February 1, 2026
• Develop a dashboard that unified the performance from all platforms into one. Unified data across platforms and display the result publicly from Tableau. • Identified WhatsApp as the primary revenue-driving channel (43% share) by tracking messaging metrics across 4 separate platforms. • Creating and interpreting the segmentation across 20 customers throughout 3 months, which has the most contributed to the revenue, and which segment needs to be concerned.
Finance Portofolio Case Study
September 1, 2025 – November 1, 2025
• Developed a comprehensive framework to assess credit risk by analyzing historical loan performance data using SQL. Engineered queries to calculate TKB30 (Settlement Rate) metrics and identified over 3,000 delinquent accounts within the portfolio. • Evaluated a 7-year historical lending dataset to isolate peak delinquency periods, discovering a correlation between higher interest rates and stressed payment cohorts. • Designed and deployed a centralized dashboard to present risk findings and borrower segmentation, translating complex SQL outputs into actionable insights for stakeholders.
Credit Card Fraud Acceptance Prediction Project
June 1, 2025 – September 1, 2025
• Cleaned complex, unstructured datasets using Python to isolate reliable anomalies and remove baseline noise • Executed deep-dive exploratory data analysis (EDA) to map applicant demographics and identify key risk trends. • Trained the data using a Random Forest Model to predict applicants and gained "good" and "bad" with 93% and 94% respectively, and a threshold was achieved at a 52% rate. • Present all the findings comprehensively by making a dashboard in Tableau, visually displaying the results of the previous findings, including both good and bad credit status at 88% and 12%, respectively.
Thesis - Sentiment Analysis
February 1, 2024 – June 1, 2024
• Developed a comprehensive research thesis involving NLP (Natural Language Processing) workflows, including data stemming, tokenization, and categorical data analysis to extract meaningful patterns from raw datasets. • Designed and deployed an interactive RShiny web application to visualize complex research findings, significantly improving data accessibility and user comprehension.
Cultural Fit Analysis
The candidate's project diversity, ranging from credit risk analysis to sentiment analysis and entrepreneurship index dashboards, indicates a broad interest in applying data analytics to various domains. The academic background in Computer Science and Statistics, coupled with a certificate in Full-Stacked Data Analytics, shows a commitment to the field. The projects align well with a Data Analyst role, demonstrating a proactive approach to learning and applying new technologies. However, the professional experience is not directly in a data analyst capacity, which might require some adjustment to corporate data team dynamics.
Soft Skills & Operational Fit
The candidate demonstrates an ability to work collaboratively (Junior Researcher role, campus initiatives) and translate complex data into understandable insights. The videographer role, while not directly technical, suggests adaptability and problem-solving under constraints. However, the professional experience is limited, and the data analytics experience is primarily academic or virtual intern, which might require more operational guidance in a fast-paced corporate environment.