Data Scientist & Data Analyst · M.S. Data Analytics Engineering @ Northeastern
I build end-to-end ML & analytics that ship to production — like a self-retraining MLB playoff predictor and a live multi-cloud GPU-pricing tracker.
Northeastern University
M.S. Data Analytics Engineering
Expected May 2027 · Boston, MA
GPA: 3.6/4.0
National Changhua University of Education
Bachelor of Business Administration
January 2025 · Changhua, Taiwan
I am a graduate student in Data Analytics Engineering at Northeastern University, passionate about turning complex data into clear, actionable business insights.
I specialize in machine learning, data visualization, and cross-functional storytelling to drive real-world business impact.
An ML system that predicts MLB games and 2026 playoff odds — five win-probability models, 10,000 Monte Carlo season simulations, retrained and republished automatically every morning, with every prediction logged before first pitch.
From 2M+ scraped records to a live GPU pricing tracker — compared AWS, Azure, and GCP on services, AI, and market share, then automated a weekly pipeline that snapshots ~5,000 GPU prices into a SQLite star schema and a live dashboard.
Mapped 16 years of U.S. aerospace patents (5,200+) across 300+ metro areas — and caught a silent data bug that had erased Los Angeles, the #1 metro, from the map; fixed it with CBSA-code joins and validated against official USPTO totals.
Analyzed 22.6K+ player-week records (2021–2024, 600+ players, 32 teams) from nflverse and built Power BI + Plotly dashboards that separate volume leaders from efficiency leaders (EPA, target share).
Turned 16,000+ records across 6 socioeconomic factors into a single 0–100 livability index ranking 17 ZIP codes, visualized as interactive choropleth maps for location decisions.
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