Sherry Shen
Trained strategist, product builder, lifelong learner, writer at heart. Phoenix rising, amateur coach, clumsy but loving mom.
Email · LinkedIn · GitHub
About
Education
- Stanford Graduate School of Business & Graduate School of Education. Expected March 2027 · Stanford, CA. MBA/MA in Education, joint degree. Arbuckle Leadership Fellow; Forté MBA Fellowship.
- Claremont McKenna College. 2016 · Claremont, CA. BA in Economics with honors, cum laude. Visiting undergraduate at Harvard University.
Experience
- Amazon. Senior Product Manager Intern · 2025 · Seattle, WA. Finished with a top performance rating and a return offer.
- SHEIN. Assistant to COO, Special Strategic Projects · 2023 to 2024 · Beijing, Nanjing & Guangzhou. The largest global e-commerce unicorn.
- Bain & Company. Associate Consultant to Manager · 2017 to 2023 · Shanghai & Beijing. Retail & Consumer Goods and Private Equity groups; promoted six months ahead of cohort.
Community
- SheShapes. Co-founder & Inaugural President · Since 2020 · Global. Non-profit women’s career training initiative; board member.
Projects
- Page2Task. 10+ users. Any page, email, or screenshot, straight to Google Tasks or Calendar.
- Learnica.ai. 300+ signups. A learning product built end to end: product, system, channel.
- Today's Plan. Coming soon. A day sorted by what matters, and a week you can name.
My earliest vibe coding projects
- The Decision. A quiet walkthrough of decision frameworks, for when you're stuck.
- Raising a Large Language Model. An explorable explanation. A model grows up in four stages, just like a person.
- Meow. A ragdoll cat that lives on a web page. Pet it, feed it, play with it.
Learning AI
A path into AI for people without a computer science background. What I read and watch to tell what is worth building, while moving toward a PM or deployment strategist role at an AI company.
Technical
How Models Work
How a model is built, and what happens when you send it a prompt.
Prompting & Context
What goes into the model, and what stays out.
RAG & Memory
Giving a model the right information at the right time.
- Complete RAG Tutorial 2026. KodeKloud. Video, 48 min. The hands-on one. You build the pipeline in the browser instead of reading about it.
Agents & Harness
How an agent plans, acts, and recovers.
MCP & Integrations
How an agent reaches tools, data, and other systems.
Coding Agents
How coding agents work, and how their output gets checked.
Evals
Measuring quality, and reading failures to find what to fix.
Traces & Observability
Seeing what happened in production.
Feedback & Data Flywheels
Turning real usage into the next version.
Safety & Security
How these systems get attacked, and what an agent is allowed to do.
Multimodal AI
Voice, vision, video, and documents.
AI UX
What the user sees, what they can undo, and what needs their approval.
Production Architecture
Running it inside someone else’s environment.
Business
Use Cases & Workflow Design
How to tell which work is worth handing to AI, and which is not.
Value & Success Metrics
Whether the thing you shipped is worth what it costs.
Cost & Latency
What a request costs and how fast it returns.
Model Choice
Which model runs which job, and how requests get routed.
Pricing & Monetization
What you charge for it.
API & Developer Platform
Running the platform other people build on.
Compliance & Data Rules
What a buyer’s security and legal team ask for.
Deployment & Adoption
Why deployments stall, and what moves them.
Market & Strategy
Where the value goes.
Builder Stories
How the people who built these products talk about doing it.