Amazon Externship via ExternI turn data into clear insights while I finish my bachelor's and prepare to start my career.
A showcase of data projects and practical work completed while I finish my degree, emphasizing applied analysis and learning.
Developed a personalized fitness app featuring onboarding, a 1000+ exercise database, and adaptive chat-driven plans. Implemented adaptive chat module enabling personalized plan updates based on progress and results. Led development of backend APIs supporting real-time updates and feedback-driven adjustments.
Engineered real-time collaborative editor with CRDT-based sync, version control, and <100ms latency. Developed microservices for real-time sync, authentication, and full-text search using AWS + Redis caching.
I turn data into clear insights while I finish my bachelor's and prepare to start my career.
I am Veedhi Bhanushali, a senior at San Jose State University pursuing a Bachelors degree in Data Science. I have some work experience and Im preparing to launch my career, focusing on data-driven problem solving and continued learning.
Externships
Amazon Operational Strategy & People Analytics Externship
In progress
Experience
Founder & Full-Stack Engineer
Jun 2024 – Present
Software Engineering Co-op
Hyundai · Aug 2025 – Present
Software Engineering Intern
UL Solutions · May 2025 – Aug 2025
Software Engineering Fellow
Headstarter AI · Jul 2024 – Sep 2024
Education
San Jose State University
Bachelors of Science in Data Science · Class of 2026
Skills
Developed a personalized fitness app featuring onboarding, a 1000+ exercise database, and adaptive chat-driven plans. Implemented adaptive chat module enabling personalized plan updates based on progress and results. Led development of backend APIs supporting real-time updates and feedback-driven adjustments.
Engineered real-time collaborative editor with CRDT-based sync, version control, and <100ms latency. Developed microservices for real-time sync, authentication, and full-text search using AWS + Redis caching.
Built a smart pantry app with image upload and recipe generation using TensorFlow.js and OpenAI. Created RecMyProf, a semantic professor recommender with Pinecone + FastAPI, serving 300+ queries across 200+ professors.
✅ 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.
The project analyzed employee reviews and operational context for Amazon fulfillment centers to surface patterns in early-career turnover. The work combined web-scraped review data with operational role mapping, then produced persona-driven analyses and strategic options for Learning Ambassadors. It documented specific behavioral patterns and proposed targeted interventions.
.png)
I produced a role-and-responsibility mapping for fulfillment-center jobs, documented cascading error patterns among Stowers, Pickers, and Packers, and delivered a consolidated spreadsheet summarizing tools, handoffs, and Learning Ambassadors' coaching touchpoints.
I mapped FC roles, their main responsibilities, who they work with, why each role matters, and common tools. I recorded how errors cascade across Stowers, Pickers and Packers and noted Learning Ambassadors' coaching role. The deliverable was a shared spreadsheet.
During the externship I cleaned the Glassdoor dataset (removed irrelevant columns, fixed dates, deduplicated) and collected and structured 10 YouTube reviews with Tone and Topic tags, producing CSV/Colab deliverables and a linked spreadsheet of examples.
I cleaned 137 Glassdoor reviews and 10 YouTube excerpts, preserved pros/cons and summaries for keyword counts, removed sparse advice-to-management content, and kept original text for sentiment scoring; a cleaned dataset and deliverable link were produced.
