Chuchu Yuan

I often hear from early-career ML/DS folks who feel stuck or from folks who want to move in AI/ML. Here’s what I’d recommend based on my own hiring experience: 💻 Reframe your resume - One page only: Focus on the last 1–2 years of most relevant work. If >10 years, add second page. Make sure you can explain each and every project/ algo in detail. - Bullet points over paragraphs: “Built and deployed an anomaly detector in PyTorch on 1M records with 95% precision” beats “Experience with PyTorch.” - Group courses vs. projects: Put certificates (DeepLearning.AI, MLOps, GCP) in a small “Training” section; highlight actual projects and results first. This can also go into the skill section. Make sure whatever you put, you've actually worked on. 💻 Show production readiness - Recruiters want more than “I trained a model.” - Emphasize (learn if needed) end-to-end work: data pipelines, Docker containers, CI/CD, cloud deployments, monitoring. 💻 Build a portfolio of small wins - Pick 2–3 mini-projects that mimic real problems: train a neural network, RAG chatbot, data ETL pipeline, simple API serving a model. Perhaps share your experience on medium/blog. - Share code on GitHub with a clear README and hosted demo (e.g., Streamlit). Create PR requests to open source projects, if possible. 💻 Ace coding challenges - Practice timed Python/data-structure problems on LeetCode or CodeSignal. Can't emphasize how important and useful this is, purely for interview. - Practice again. Even when not interviewing. 💻 Network with purpose - Ask for 15-minute “resume reviews” or quick feedback on a project. - Engage on Discord communities (e.g., MLOps, DataTalks) — personal referrals carry weight. Landing that first ML role can feel like an uphill climb, but every resume tweak, project post, or friendly referral moves you closer. Keep iterating—and you’ll break through.

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Getting a job in analytics is hard…so let’s talk about it. I see the same question come up again and again: “How do I get hired as an entry-level analyst?” And the advice is almost always the same: – Apply to as many roles as possible (it’s a numbers game) – Copy and paste cover letters – Memorise interview answers But what if there’s another way? I started in analytics over a decade ago, and my route in was anything but conventional. No neat graduate-level title. No clear progression plan. And it ended up shaping the type of analyst I am today. If this sounds a little too familiar, then this one’s for you 👋🏽 🔗 Link to my first Medium article in comments

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We're hiring three AI Analytics Engineers on the Data Science & Analytics team at Airtable. These are early to mid-career roles for builders who are excited about working at the intersection of data science, analytics, and AI. One of these roles will report directly to me as we spin up a new AI & Analytics Platform function. Things are moving fast. The most impactful work in data right now isn't building models — it's fundamentally changing how a company gets answers from its data. That's where my focus is, and it's why Airtable is investing in a new team to own it. Over the past six months, our team stopped experimenting with AI on the side and restructured around it: - Agent-powered development workflows — I built shared AI systems for the data team embedded in how we write SQL, build pipelines, document business logic, and debug. Team members are regularly seeing 2x to 10x speed improvements — not from shortcuts, but from AI woven into every step of how we build. - Conversational analytics — Ad-hoc questions that used to fill our queue from leadership, product, GTM, and finance are increasingly answered by an AI agent — in natural language, where people already work. -A context layer that makes it trustworthy — We built a system that gives the agent real business knowledge — which table is correct for a given analysis, how metrics differ across teams, why something spiked last quarter. Context that usually lives in an analyst's head. - A learning loop that compounds — Every user correction becomes a permanent improvement, not a one-off fix. — We're doubling down. Airtable is standing up an AI & Analytics Platform function within DS&A to keep building at the intersection of data science, ML, analytics, and AI — and I'm leading it. We need more builders. AI Analytics Engineer, AI & Analytics Platform First hire on this team. You'll build the context layer that makes AI tools accurate, design evaluation frameworks that make them trustworthy, and develop agent systems that let the org self-serve with confidence. You'll work directly with me to shape what this function becomes. https://lnkd.in/gFstQqQb We're also hiring two roles embedded in other parts of the business: AI Analytics Engineer, Marketing Analytics Own data infrastructure, dashboards, and AI-native tooling for Marketing. Build the canonical data sources leadership relies on. https://lnkd.in/gYHS55ma AI Analytics Engineer, Business Analytics Own financial data models powering ACV, ARR, billings, and revenue. Partner with a Finance team that's building their own AI agents and workflows. You'll build the data foundation they run on. https://lnkd.in/gV5hnJjG — I'm looking for people who build before they're asked. Who have a side project that started as "I wonder if I could..." and turned into something real. Who pick up a new tool and immediately try to break it. If that sounds like you — reach out or apply. I'd love to talk.

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Why mid-market Data Science teams can’t keep up Mid-market companies don’t have a data problem. They have a decision throughput problem. We see this pattern over and over again: • 2–10 data scientists • A backlog measured in months, not weeks • Teams stuck in analytics firefighting • Execs asking “what would happen if…?” • Answers arriving too late to matter The bottleneck isn’t talent or tools. It’s that Data Science has become the choke point for every important decision. Here’s why it breaks down: 1. Data Science capacity is scarce - Every new initiative competes for the same few people. - Most DS time gets burned on ad-hoc analysis, one-off models, and stakeholder explanations. 2. Time-to-impact is too slow - By the time an analysis is done, the decision window has passed. - Accuracy improves. Business outcomes don’t. 3. Talent retention is fragile - When decision logic lives in notebooks and individuals, the organisation is one resignation away from losing critical knowledge. 4. Executives need causal answers, not forecasts - “Will revenue go up?” isn’t the question. - “What happens if we change pricing, promos, or capacity?” is. Most teams can’t answer that fast enough. The uncomfortable truth: 👉 Hiring more data scientists doesn’t fix this. 👉 Better dashboards don’t fix this. 👉 More advanced ML doesn’t fix this. The constraint is decision velocity. The teams that break through don’t try to make Data Science faster. They make decision-making scalable, by turning bespoke analysis into repeatable, decision-grade systems that more people can use. That’s the shift from analytics as a service… to analytics as a decision engine. Curious how other mid-market teams are tackling this. What’s your biggest decision bottleneck right now?

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I Made It Up new feature drop: Analysis. This is a new tool that processes synthetic interview transcripts for quantitive (numbers) and qualitative (stories) analysis: • Quantitative - means + variance + statistical comparisons across demographic groups + outlier detection • Qualitative - theme extraction + clustering into higher-level patterns + supporting quotes Insights before you're back with coffee. If you don't know: I Made It Up is a synthetic research toolkit that plugs straight into AI chat like ChatGPT and Claude. (If that's jargon, 'synthetic research' is methodology that uses AI to simulate digital personas as avatars to explore how people might think. Made up? Yes. Useful? We think so.) Big words. What's it like in practice? Say you interview 36 personas about a product idea. Your AI then passes the transcripts to the Analysis tool for processing. You + AI then get results back that enable further analysis + thinking. Quantitative tells you: "Gen Z rated this 2.1 out of 5, while Millennials gave it 3.8 - that's statistically significant with a large effect size". Qualitative tells you: "Three themes emerged - price sensitivity, trust concerns, and feature confusion - here are the quotes that back each one up". Super early stage. Interviews are limited to six questions for six personas. This goes up soon and when it does, the Analysis tool becomes indispensable. Want to know more? If you're already using it, just ask ChatGPT or Claude what you can do with I Made It Up's Analysis and Retrieve tools. If you're not yet, check Getting Started on website linked in comments. Available on Pro accounts and above. Lots under-the-hood to unpack. Blog soon. Questions? Hit me up. Would love to talk and if you're local, can drop in to your office for a chat. 

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Hello friends -- Here is a quick tutorial on how to make a custom export for your AI/BI dashboards in Databricks. This is particularly useful if you have a dashboard with html formatting. Thank you for reading!

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Most data scientists can run an A/B test. Few can actually design one that holds up. Ever had a PM ask you mid-experiment: "Can we just check the results now?" Or shipped a test only to realize your sample size was way off? These gaps don't show up in tutorials. They show up in real meetings with real stakes. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐠𝐨𝐨𝐝 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐞𝐫𝐬 𝐟𝐫𝐨𝐦 𝐠𝐫𝐞𝐚𝐭 𝐨𝐧𝐞𝐬: ↳ Knowing when to use sequential testing vs. fixed-horizon ↳ Catching peeking, selection bias, and Simpson's paradox before they wreck your conclusions ↳ Using CUPED to reduce variance and speed up experiments ↳ Translating p-values into decisions that leadership actually trusts If you want to go deeper on this, there's a course I came across that covers exactly these gaps: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬 on Maven: https://lnkd.in/dqai228G ↳ Taught by Manisha Arora (DS Lead @ Google) and Banani Mohapatra (DS Manager @ Walmart) ↳ Weekend intensive: Feb 7-8 ↳ Covers experiment design, statistical methods, Bayesian testing, multi-arm bandits, and stakeholder communication ↳ Get $250 off with the code: 𝐒𝐀𝐈201 Worth checking out if you're serious about leveling up your experimentation game. What's the trickiest part of A/B testing for you? 👇 ♻️ Repost to help a data scientist who's ready to go beyond the basics

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The most underrated challenge every analyst faces? → Solving the real problem, not the one that’s asked. Too often, analysts get requests like: “Pull this number.” “Build this dashboard.” “Can you slice this by X/Y/Z?” But the truth is: Most of these asks are symptoms, not the actual problem. And this is where analytics becomes hard — not because of SQL, Python, or dashboards… …but because we’re expected to translate vague questions into actual business problems. The real challenge is: 🔹 Understanding why the stakeholder wants the metric 🔹 Challenging assumptions without sounding defensive 🔹 Turning unclear requests into decision-making clarity 🔹 Protecting time from low-value analysis 🔹 Making sure the output drives action, not just slides Great analysts don’t just answer questions. They help teams ask better questions. If you master that… the tools become the easy part. #Analytics #DataAnalytics #ProductAnalytics #BusinessIntelligence #SQL #Python #DecisionMaking #DataCulture #AnalyticsLife #CareerInData #ProblemSolving #DataDriven #Leadership #AnalystLife

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Deinfluencing you as an ex-Senior Data Analyst in Tech: 1. I never want to work at FAANG and think it's highly overrated.     2. I don't code or do data projects for fun on the weekends.     3. I love putting tables on dashboards so stakeholders can export data to Excel.     4. People can learn data skills on their own & be super successful without a formal education.     5. Overly data-driven cultures can be toxic and lead to analysis paralysis.     6. Instead of obsessing over new trends like LLMs and Gen AI, I've specialized in the bread and butter of analytics (SQL & data viz!)     7. Data storytelling can be manipulative and deceiving.     8. Data isn't always as secure and protected as you think it is.

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𝐀 𝐭𝐨𝐨𝐥 𝐭𝐡𝐚𝐭 𝐠𝐞𝐧𝐮𝐢𝐧𝐞𝐥𝐲 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐡𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤. As a TA for Data Engineering, I juggle Snowflake, pipelines, student reviews, and demo prep. Snowflake Cortex Code has become my go-to for all of it. 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐬𝐭𝐚𝐧𝐝 𝐨𝐮𝐭: 𝐏𝐫𝐞𝐛𝐮𝐢𝐥𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 : Domain-specific skills for data governance, dynamic tables, cost intelligence, data quality, Streamlit, Iceberg, semantic views, dbt, and more. Like having a Snowflake consultant on call. 𝐒𝐮𝐛-𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐀𝐠𝐞𝐧𝐭𝐬 : Specialized agents work in parallel. one exploring code, another planning, another validating SQL. Background agents run while you keep working. 𝐌𝐨𝐝𝐞𝐥 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 : Multiple underlying models. Pick the right one for deep reasoning or quick generation. 𝐖𝐡𝐚𝐭 𝐬𝐞𝐭𝐬 𝐢𝐭 𝐚𝐩𝐚𝐫𝐭 𝐟𝐫𝐨𝐦 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞? Cortex Code is Claude Code built natively into Snowflake with prebuilt skills, Snowflake-aware agents, and direct warehouse access. It understands your data, warehouses, pipelines, and costs. No general-purpose tool matches that. 𝐈𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞:  • Planned an entire multi-tenant streaming platform in one session : architecture, governance, testing, CI/CD, all with actionable checklists  • Cost analysis, data profiling, and lineage tracing without leaving the terminal  • Streamlit dashboards for class demos using the dedicated skill  • Explored dbt project lineage, validated models, and debugged pipelines directly through the dbt skill This lets me focus on teaching, not boilerplate. Massive appreciation to the Snowflake team. This is what developer tooling should feel like thoughtful, powerful, and deeply integrated. #DataEngineering #Snowflake #CortexCode #BuildInPublic #AI

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Join us in building world-class AI products for Google Finance and for many CFO organizations in the world. Full details below!

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