Rizky Efendri

Designed an automated OSINT data pipeline utilizing Python, FastAPI, and MongoDB to scrape, sanitize, and store large volumes of multi-platform social media and web data. Optimized backend performance and cost-efficiency by engineering strict execution limits and refining data schemas for external scraping APIs, preventing redundant data processing. Resolved critical data integrity bottlenecks by implementing robust UTF8 text sanitization and truncation algorithms for large-scale data ingestion and database writes. Built a multi-turn conversational AI API endpoint that allows users to interactively query background reports, handle profile disambiguation, and trigger new investigations asynchronously. Developed robust concurrent background worker queues in Go and Python to prevent hanging jobs and ensure the reliable execution of automated scouting and data enrichment tasks.

Enabled more informed strategic decision-making by effectively addressing diverse business challenges with data-driven problem-solving. Managed end-to-end data science lifecycle, including rigorous data cleaning, model training, performance evaluation, and supporting production deployment. Contributed to the development of internal analytics tools and fostered cross-functional collaborations with product and engineering teams to integrate data solutions.

Completed intensive coursework in Data Science, Business Ethics, and Quantitative Research Methods through a virtual exchange program. Designed and delivered a comprehensive mini-course on foundational Data Science and Business Ethics concepts as a final program project.

Artificial Intelligence Mastery Program

Orbit Future Academy

Intermediate Machine Learning

Kaggle

Python Fundamental

Progate