Machine Learning Data Science

Chien (Angus) Ho Email :*****.*****.**@*****.***

Mobile : +1-312-***-****

LinkedIn Link

Education

The University of Chicago, M.S. in Applied Data Science Chicago, IL, Sep 2024 - Dec 2025 Related Coursework —Big Data and Cloud Computing (Hadoop, Spark), Statistical Models (GLMs), Time Series National Taiwan University, B.A. in Economics Taipei, Taiwan, Sep 2018 - Jun 2023 Related Coursework — Linear Algebra, Machine Learning, Deep Learning, Econometrics, Statistics, Text Mining (NLP) Publicatioins

• Chia-Yen Lee, Kai Chang & Chien Ho ”Autoencoder-based detector for Distinguishing Process Anomaly and Sensor Failure”, International Journal of Production Research. Co-authored with Deputy Director, AI4BI, TSMC Taiwan. Work Experience

Taiwan Mobile, Data Science and Machine Learning Intern Taipei, Taiwan, May 2023 - Dec 2024

• Implemented a TensorFlow-based Autoencoder anomaly detection framework with KS tests and KL Divergence to continuously monitor feature distributions, significantly reducing monthly model retraining time by 7 days.

• Optimized the Retrieval Augmented Generation (RAG) pipeline by integrating LanceDB vector databases and LangChain for hybrid vector similarity search, boosting retrieval accuracy by 50%. Shopee, Supply Chain Data Analyst Intern Taipei, Taiwan, Sep 2022 - May 2023

• Designed and maintained KPI dashboards using Power BI and MySQL to monitor key supply chain metrics, ensuring alignment with operational and financial targets.

• Developed MySQL-driven real-time warehouse capacity dashboards to prevent putaway conflicts, ensuring seamless inventory operations and increasing peak warehouse capacity by 10%.

• Automated replenishment processes using Google Apps Script for ETL and integrated real-time sales and inventory data, reducing manual effort by 80% and enabling just-in-time inventory management. Projects

Rakuten Advertising, Capstone Project Chicago, IL, Feb - Dec 2025

• Developed a scraping engine (POC) using Octoparse, Scrapy, and BeautifulSoup to extract detailed display ad data

(product info, ad type, content, advertiser info, time, and location) from top publisher websites.

• Accelerated data extraction by implementing parallel processing and scheduling scrapers to run multiple times daily over several months, ensuring timely updates.

Starbucks Expansion Recommendation, Machine Learning Term Project Chicago, IL, Feb 2025

• Engineered location-based features (POIs, demographics, isochrones) from diverse data sources (Google Places API, OSM, Census) and performed advanced feature selection (Kruskal-Wallis, Boruta) to optimize model accuracy.

• Executed geospatial clustering (DBSCAN) and built classification models (Logistic Regression, XGBoost) to predict store success (rating above 4 stars), improving decision-making for new locations. Research Experience

Production Optimization Lab, NTU, Research Assistant Taipei, Taiwan, March 2023 - Present

• Engineered a real-time anomaly detection pipeline leveraging Autoencoders, Isolation Forest, and One-Class SVM

(TensorFlow/PyTorch, pandas), improving the F1-score and reducing false positives in production signal analytics.

• Utilized Operations Research methods (LP) with Gurobi to measure scope efficiency and inform strategic product mix decisions, enabling companies to optimize performance based on variety rather than sheer volume. National Central University, Research Assistant Taipei, Taiwan, July 2022 - Feb 2023

• Deployed a Python Selenium web crawler to scrape 100,000+ reports, optimizing performance through multiprocessing.

• Conducted sentiment analysis using NLP techniques to convert natural language data into quantitative variables. Econometric Term Paper: Time Series Analysis, Group Leader Taipei, Taiwan, March 2022 - May 2022

• Employed R and Stata to clean data, conduct time series regression, and run hypothesis tests, uncovering correlations between higher education and total factor productivity to guide future economic research. Skills Summary

Programming Languages: Python (PyTorch, Keras, scikit-learn), C/C++, R, Stata, SQL, Google Apps Script, JavaScript Tools: Power BI, Tableau, Google Studio, Git, GCP, AWS (S3, EC2, Lambda), Azure, Airflow, Docker

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