Yu Dong

Data Science & Analytics, Data Storyteller

4 minute read

My Medium Articles!

In the past two months, I continued writing articles per month on Medium and Towards Data Science. Here you go:

  1. Can a Non-Software Engineer Vibe Code a Real App with AI? I Tried It.: During a short break between jobs, I challenged myself to vibe code a personal finance tracker—something simple but useful. In 20 hours, I went from idea → PRD → working app → deployment using ChatGPT, Cursor, and Codex…and lots of curiosity. This article documents my experience and my learnings.
  2. How I Used ChatGPT to Land My Next Data Science Role: The job search process can feel overwhelming, but tools like ChatGPT turn it into a more structured journey. In my latest article, I shared how ChatGPT acted as my personal career coach, helping me stay organized, efficient, and confident.

Reading List in Past Two Months

Before talking about articles, I actually read one book in September – Matchmakers: The New Economics of Multisided Platforms. As I got three-weeks break before starting at DoorDash, I decided to read a book to learn more about the marketplace business. This book did a through walkthrough of the history of marketplace business and its common challenges.

Now, let’s talk about the great articles I came across in the past two months.

Data Science & Analytics

  1. The Machine Learning Lessons I’ve Learned This Month: Simple but practical tips to make model development process better.
  2. Causal Inference in Data Science: Beyond Correlation: What’s the limitation of prediction modeling and common methods in causal inference.
  3. Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need: Important evaluation metrics beyond accuracy in machine learning.
  4. From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix: Neflix team shares how they built their Media ML Data Engineering function.
  5. Behind the Numbers: How Meta’s Data Engineers Bring Transparency to Life: Meta team introduces how they build governance systems that influence what production systems are allowed to do in the first place.
  6. How DoorDash Ads keep consumers first with budget A/B experimentation: The DoorDash Ads team introduces the budget A/B framework to ensure fair and unbiased results by creating separate universes within a single campaign, splitting the budget across treatment groups.
  7. Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale: Muse equips creative strategists and launch managers with data-driven insights showing which artwork or video clips resonate best with global or regional audiences and flagging outliers such as potentially misleading (clickbait-y) assets.
  8. My Starter Project on the Lyft Rider Data Science Team: The author shares how they used Rider Experience Score (RES) tool to measure long-term effects of various rider experiences at Lyft.
  9. Data as a Product: Applying a Product Mindset to Data at Netflix: Key principles of data products and what does it mean at Netflix.
  10. Data Quality Statistics & Insights From Monitoring +11 Million Tables: The Monte Carlo team shares the common data quality incidents root causes and trend on their platform.
  11. Why I Stopped Using CTEs Everywhere in SQL: When CTEs are actually bad and why temp view or tables could be better.

Data Career

  1. How I Went from Non-Tech to Senior Machine Learning Scientist at Amazon: The author shared his inspiring experience of entering the ML space with a non-tech background.
  2. WTF is Building Trust in Data?: When we talk about building trust, what exactly are we referring to?
  3. The 7 Habits of Highly Effective Leaders: Common traits of great leaders, including embracing failure, being authentic, ingoring selectivity, embracing imprefection, being transparent, cultivating empathy, and embracing change.
  4. Most Graduate Degrees In Analytics Are Scams: The author shared his view on why the graduate degrees in analytics are always misaligned with the data science career requirements.
  5. The Human Touch in Data Science: 4 Skills AI Can’t Mimic: Important data science skills in the age of AI, such as using domain knowledge, finding data quality issues, optimizing AI outputs, and interpretation.
  6. The Silent Data Skills That Make You Unfireable in 2025: Seven skills that are critical to data scientists.
  7. I’m a Senior Data Scientist. Here’s Why I’m Not Worried About AI.: AI is creating a new dividing line, and those who adapt will become more valuable than ever before, while those who don’t will struggle.
  8. Most data analyses die in silence. Here’s how to fix that.: Most anlaysis go nowhere not because they are bad, but because they are poorly presented.
  9. Why Every Data Scientist Should Think Like an Economist: How the data scientist role has evolved over time, and whye we should shift from pattern recognition to mechanism understanding like economists.

AI and LLM

  1. From Data to Decisions: Applying AI to Product Market Intelligence: AI uses cases in Product Market Intelligence for the GTM team.
  2. AI FOMO, Shadow AI, and Other Business Problems: AI is a tool, not magic. So the key here is to apply AI solutions thoughtfully and carefully.
  3. 7 Things I Wish Someone Told Me Before Building My First Vibe -Coding App: Great tips for anyone who is interested in emboarding their own vibe-coding journey.
  4. AI Clarity Engine: How to use AI as a clarity engine, instead of just writing emails or summarsing things.
  5. Vibe Analysis: Conversational Data Exploration with Streamlit and LLMs: The author walks through the Vibe Analysis framework they built.
  6. The AI Bubble Is About To Burst, But The Next Bubble Is Already Growing: The author shares why he thinks AI is a bubble and the growing new bubble of quantum computers.
  7. OpenAI Atlas: Beyond the Browser Window — Unpacking Agentic Web, AI Memories & The Future of Online Interaction: Important new capabilities unlocked by OpenAI Atlas and concerns.

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