Yiqing Liu

I feel it’s only a matter of time before we start hearing the question: “Did you consult with ChatGPT?” whenever complex topics come up at work. For fact-based, technical discussions, I’ve already started linking my ChatGPT consultations in emails—almost like citing a reference. It also makes me wonder about future possibilities: what if ChatGPT enabled teams to brainstorm in the same chat together, in real time? That could unlock a whole new dimension of collaboratio

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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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📨 One of the biggest struggles I often hear from folks in the industry is how hard it is to convince leadership to allocate headcount to ML teams. But very little is said about what to do once you actually get the headcount. You’ve got approval to hire, maybe a couple of people this year. That’s exciting, but also one of the biggest risks for a new leader. Few things erode trust faster than hiring the wrong people and burning through those slots, not to mention the negative impact it can have on the team you already have! I wrote about this and all the questions worth asking before opening *that* role in the latest newsletter post here: https://lnkd.in/d3betgHV

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In my recent conversations, I've noticed a 2 concepts that seem to be causing some confusion in the DS community, and I'd like to take a moment to clarify them: 1. Difference in Bias of an estimator and Bias variance trade off. 2. Covariate shift in causal inferencing vs feature drift. Bias in the Estimator refers to a single, standalone property of a statistical model's prediction. An estimator is considered unbiased if, on average, it hits the true value of the parameter it's trying to estimate. In other words, if you were to repeat your experiment many times, the average of your predictions would equal the true value. Whereas, The Bias-Variance Trade-off is a concept that describes a fundamental tension in model building for choosing the complexity level. Covariate shift in causal inference and feature drift in machine learning are related concepts, but they refer to different problems and appear in different contexts. Covariate Shift in Causal Inference describes a situation where the distribution of the input variables (covariates) in the test data is different from the distribution in the training data, while the relationship between the covariates and the outcome variable remains the same. In a causal context, this is a big problem because causation is lost which Invalidates Causal Conclusions. Feature drift, or concept drift, is a broader term in machine learning. It refers to the problem where the statistical properties of the target variable or input features change over time in an unexpected way.  Hope this help to the readers.

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Why multi-region #BigQuery setups silently break #Looker logic Just because your warehouse runs, doesn’t mean your dashboards do. Multi-region BigQuery setups look good on paper: 📌 Lower latency 📌 Regional redundancy 📌 Distributed compute scale But they wreak havoc on: 📌 Cross-region joins 📌 #Looker #PDT materialization delays 📌 Cost spikes from data egress What breaks: 📌 PDTs fail silently if region mismatched 📌 Join logic breaks due to location-based restrictions 📌 Long refresh cycles frustrate stakeholders Design smarter: 📌 Keep Looker connections region-consistent 📌 Materialize PDTs in same location as base tables 📌 Audit data transfer costs weekly Want consistent dashboards??? Start with consistent regions. Repost to help others. Check these links to learn more: BigQuery region/multi-region restrictions https://lnkd.in/daedvRgj Looker PDT region configuration https://lnkd.in/dMMDxCFM Looker BigQuery connection setup https://lnkd.in/dpzmJysQ BigQuery data egress and transfer pricing https://lnkd.in/dFCPSzHi Data transfer and location requirements https://lnkd.in/dmRBsK99 Looker Google Cloud #GCP  #Looker  #LookerDocumentation   #Data  #DataAnalysis  #DataVisualization  #GoogleCloud #Developer #Dashboard #DataScience #BigData

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Enterprise AI has a dirty secret. The models work, the technology is ready, the demos are dazzling. But inside real companies - Most AI deployments are failing quietly. Not because of the technology. Because of the LAST MILE - The part where AI meets: → data that hasn’t been touched in a decade → systems that were never designed to talk to each other → workflows that exist only in someone’s head → stakeholders who can’t agree on what success looks like What role that is disrupting and transforming the world of AI?

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