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Data Science with 4+ years in Data Analysis & Machine Learning
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A Cyber Security graduate from Binus University with hands-on experience in penetration testing, security policy analysis, and business operations. Currently on a career-switching track to Data Science/AI/Machine Learning by joining Hacktiv8 Bootcamp. Proficient in Indonesian (native) and English (advanced).
Hacktiv8
Full-Time Data Science Program (FTDS)
February 1, 2026 – May 1, 2026
Binus University
Bachelor of Computer Science · Cyber Security Major
September 1, 2017 – December 1, 2022
PT Victory Sukses Bersama
Operation Assistant
May 1, 2023 – March 1, 2025
Jakarta, Jakarta, Indonesia
PT BDO Konsultan Indonesia
Penetration Tester (Internship)
March 1, 2020 – February 1, 2021
Jakarta, Jakarta, Indonesia
PT Martin Rose Indonesia
Customer Service Representative
January 1, 2019 – February 1, 2020
Jakarta, Jakarta, Indonesia
Vehicle Type Recognition App (Computer Vision Project)
April 1, 2026 – April 1, 2026
Build an end to end computer vision classifier to identify vehicle type across 4 classes: bus, car, motorcycle, and truck. Improve model accuracy from 57% with custom CNN baseline to 96% using MobileNetV2 transfer learning, image augmentation, Batch Normalization, Dropout and Tensorflow Callbacks. Evaluated model performance using classification reports, confusion matrix analysis, training/validation curves, and class-level precision, recall, and F1-score. Deployed an interactive Streamlit image-classification app on Hugging Face Spaces with Docker, allowing users to upload vehicle images and receive predicted class plus confidence score.
Airbnb Market Readiness Analysis (Data Engineer Project)
April 1, 2026 – April 1, 2026
Built an automated ETL pipeline using Apache Airflow with 3 sequential tasks (fetch_from_postgresql → data_cleaning → post_to_elasticsearch), scheduled via cron and containerized with Docker Compose. Cleaned and engineered 292,802 Airbnb listing records across 7 global cities, normalizing column names to snake_case, handling missing values, and creating 4 derived features (has_price, has_license, host_segment, listing_key). Validated data quality using Great Expectations with 7 custom expectations covering uniqueness, value ranges, type checks, and set membership — all passing with success: true. Indexed clean data into Elasticsearch using streaming bulk API and built a Kibana dashboard with 6 visualizations (horizontal bar, vertical bar, pie chart, data table, line chart, area chart) analyzing listing supply, availability, room type, license coverage, review trends, and host segmentation across 7 cities. Delivered data-driven market expansion recommendations, identifying Rome as the top priority based on balanced demand, availability, and strong licensing compliance.
Data Science Salary Prediction App (Data Science/ML Project)
March 1, 2026 – March 1, 2026
Engineered a regression-based salary prediction system for Data Science role in the US, achieving R2 score of 0.70 through rigorous model selection and hyperparameter optimization. Evaluate multiple algorithms including Random Forest, Gradient Boosting and Decision Tree, utilizing RandomizedSearchCV to minimize Mean Absolute Error(MAE) and enhance prediction accuracy. Developed and launched web-based application using Streamlit, providing users with real time salary estimation. Built robust transformation pipelines using Scikit-Learn's ColumnTransformer, automating feature scaling, missing value imputation, and categorical encoding for complex job metadata.
Indonesian E-Commerce Order Cancellation Analysis (Data Analyst Project)
December 1, 2023 – November 1, 2025
Analyzed 208,484 e-commerce transactions (Dec 2023 – Nov 2025) to identify root causes of a 13.57% order cancellation rate, covering cancellation reasons, payment methods, shipping options, and monthly trends. Performed data cleaning including datetime conversion and null imputation on 18,018 missing cancellation reason values using context-based logic. Applied descriptive statistics and Mann-Whitney U Test to compare shipping cost distributions between cancelled and completed orders; produced 4 data visualizations.
Cultural Fit Analysis
The candidate is actively transitioning into Data Science through a bootcamp, indicating strong motivation and adaptability. The academic projects demonstrate a proactive approach to learning and applying diverse data science techniques. The blend of a Cyber Security background with a new focus on Data Science suggests a broad perspective, which can be beneficial for innovative problem-solving. However, the lack of professional data science experience means cultural fit within a fast-paced, senior data science team is yet to be fully proven.
Soft Skills & Operational Fit
The candidate's previous roles as Operation Assistant and Customer Service Representative suggest experience in problem-solving, client interaction, and contributing to strategic discussions, which are valuable for understanding business context in data science. The penetration testing internship indicates an analytical mindset and attention to detail. However, these roles are not directly technical in the data science domain, and the descriptions are somewhat generic, making it hard to assess the depth of operational fit for a senior data science role.