Currently an Extern @Amazon·🟢 Open to work
I’m a senior at UC Irvine, blending tech skills with market insights to drive impactful business solutions.
Dive into my portfolio showcasing diverse projects in UX research, market analysis, and software development.
Built a full-stack search engine in Python across 3 milestones — implemented inverted index, TF-IDF ranking, and cosine similarity scoring; owned architecture documentation and scope decisions; coordinated 3-person team via GitHub. Used AI tools throughout research, documentation, and validation phases; presented technical deliverables clearly to both technical and non-technical reviewers.
Owned end-to-end product cycle for a mobile app — conducted user research interviews, built Figma prototypes, defined requirements, and ran structured usability evaluations with real participants. Produced formal deliverables including user stories, acceptance criteria, process documentation, and usability reports communicating findings and recommendations to stakeholders.
I’m a senior at UC Irvine, blending tech skills with market insights to drive impactful business solutions.
I’m Liam Fitzpatrick, a senior pursuing a Bachelor's Degree in Business Information Management at UC Irvine. With hands-on experience in client services and research, I blend technical skills in Python and SQL with a passion for market analysis and product design. I aim to bridge the gap between technology and business to drive impactful solutions.
Externships
Amazon Operational Strategy & People Analytics Externship
Amazon
Experience
Client Services & Research Assistant
Harvard Tenant Advocacy Project · 2025 – 2026
Barista & Inventory Lead
Lilikoi Boba · 2024 – 2025
Center Attendant
UC Irvine Housing — Mesa Court · 2023 – 2024
Education
University of California, Irvine
B.S. in Business Information Management · Class of 2027
Skills
Built a full-stack search engine in Python across 3 milestones — implemented inverted index, TF-IDF ranking, and cosine similarity scoring; owned architecture documentation and scope decisions; coordinated 3-person team via GitHub. Used AI tools throughout research, documentation, and validation phases; presented technical deliverables clearly to both technical and non-technical reviewers.
Owned end-to-end product cycle for a mobile app — conducted user research interviews, built Figma prototypes, defined requirements, and ran structured usability evaluations with real participants. Produced formal deliverables including user stories, acceptance criteria, process documentation, and usability reports communicating findings and recommendations to stakeholders.
Conducted market basket analysis, App Store platform strategy evaluation, and software pricing optimization — translating quantitative findings into clear business recommendations for stakeholders.
✅ Verified by Extern · ⏱️ In progress
Imagine shaping how AMAZON — yes, THE Amazon — welcomes and keeps its workforce. Every year, Amazon loses billions because new hires leave before Day 90. You’re going to fix that. In this externship, you’ll web scrape real-world employee reviews from across the web, extract actionable insights, and design persona-driven strategies to help Learning Ambassadors connect smarter, faster, and more humanely. It’s part research, part strategy—and all about learning how people data drives big decisions.
I engaged in an externship with Amazon focused on enhancing their operational strategy through people analytics. The project aimed to address employee turnover by web scraping employee reviews, extracting insights, and developing effective strategies to improve the onboarding experience. My work contributed to creating a more connected and supportive environment for new hires.
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I produced a structured briefing note that validated high-turnover claims and the Pay-to-Quit program using public reports and cited third-party sources.
Faced high turnover at fulfillment centers, I reviewed reports and company practices, drafted a briefing note that summarized background and analysis, and produced recommendations to clarify voluntary versus company-driven attrition.
Situation: raw Glassdoor reviews contained duplicates, empty columns, and inconsistent encodings. Task: prepare an analysis-ready dataset. Action: removed duplicate rows, dropped useless or empty columns, normalized fields, and exported a clean CSV. Result: a tidy 139-row Glassdoor dataset…