Chen Yi

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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Yesterday I read this great article for the upcoming EMNLP about product review summaries from a few folks at Wayfair: End-to-End Aspect-Guided Review Summarization at Scale by Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joe Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson Retailers: this is how review summaries should be done. Wayfair’s new paper shows an end‑to‑end, aspect‑guided approach that turns thousands of raw reviews into a short, trustworthy product summary shoppers can actually use. What’s novel for retail: - Evidence‑first UX: The model writes ~300–500‑character copy anchored to representative reviews. Shoppers can tap aspects like comfort or delivery to dive in. - Real‑time ops: Auto‑generate once a product hits 10 reviews and then auto‑refresh when new reviews grow enough. - Built for scale: Cached aspect mappings + sampling keep costs and latency in check without losing signal. - Proven impact: AB testing shows lifted ATC and CVR and cut bounce with no hit to revenue or page speed. - Open data: ~12m anonymized reviews across 92k products with extracted aspects + generated summaries available on Hugging Face. Open data is a key to comparing models across industries and use cases. Why it matters: A trustworthy, interpretable, continuously fresh review UX that boosts confidence and carts.

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The 2025 State of AI (through Karpathy’s lens) - it’s not “the year of agents.” It’s the decade. Recently, Andrej Karpathy was interviewed on the Dwarkesh podcast (I'll put a link in the comments), and this is what he said on the current state of AI agents: 1) Creating AI agents that work reliably is a project that will take up to 2035 to complete. We don’t yet have the scaffolding for coworker-level agents: durable memory, reliable multimodal perception, continual learning, robust tools/OS control. This is a marathon of engineering, not a quarter’s roadmap. 2) Software progression from 1.0 to 2.0 to 3.0 1.0: humans write code 2.0: data writes weights 3.0: we program via language + LLMs Don’t skip the representation era. Strong “cognitive core” comes first, agents second. 3) We’re building “ghosts,” not animals. Models simulate human behavior from data. No instincts. No body. Expect brilliance in patterning, less so in grounded, embodied common sense (at least for now). 4) Reasoning lives in the context window. Weights are kind of like long-term memory. Context is kind of like working memory where thinking happens. Feed richer context, get sharper reasoning. Retrieval is way better than wishful prompting. 5) Only part of the ‘brain’ exists We have Cortex-like patterning. We don't have Hippocampus-like memory consolidation, emotion/instinct, coordination. That’s why current systems “think,” but don’t truly embed. 6) Building beats hand-wavy prompting Use AI to accelerate, not abdicate. Autocomplete helps but novel system design still needs humans who understand what they’re building. 7) RL is inefficient, process beats outcome-only rewards. Next step: process-based supervision and reflection loops. Teach models to review, self-correct, and generate useful training data. 8) Forgetting can improve generalization. Humans generalize because we forget. Models that memorize everything risk rigidity. Curate, prune, and reinforce abstraction, not trivia. 9) Bigger isn’t always better. Clean, high-quality data + smart architecture can outperform “just scale it.” A lean, well-trained cognitive core can feel startlingly capable. So, to summarize, here are a few practical takeaways: * Design for context: Retrieval + tool use + structured memory. * Instrument the process: Critique, reflect, and fine-tune steps, not just outputs. * Curate ruthlessly: Data hygiene is a moat. * Ship scaffolding: Memory, tool orchestration, reliability. Think in decades, execute weekly, and compound small real improvements. Let's not forget that we’re mid-build, not at the finish line. The transformer will likely be with us a while, just refined. The winners will be the ones who build patiently, instrument relentlessly, and curate obsessively. Let me know your thoughts in the comment section

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4 years at Uber today. My most valuable lesson: grow people’s Data Science and TPM skills to supercharge collabs. This is how I did it 👇 HOW: - Composed a 100+ page document called “Data Science For Everyone”, which is equipped with step-by-step tutorials, videos and notes that accelerate and orientate practical skill development for real problems - Maintain the Guide current with regular updates, engaging visualizations (where possible) and notes that complement Source of Truth documentation elsewhere -Circulated passively and engaged comments/suggestions inline and via slack, encouraging others to share if they found it valuable - Made myself available to chat, zoom and present IMPACT: - 1.2k+ users across the business - 50+ teams, involving professionals such as: MLE, SWE, Data Scientists, Data Engineers and TPMs/PMs across Safety, Risk, Legal, Strategy & Ops and many others - 400+ distinct users per month for the last few months PURPOSE: - Grass-root tool to onboard new hires - Teaches how to use GenAI tools - Architectural designs for experimenting with simple LLM use to multi-agentic RAG workflows - Generic code bases for people to get started - Standard project directory designs that illustrates best practices for scalable and efficient growth - Guidance and code snippets for how to use Python for big data projects, including how to build automation and classic ML solutions CIRCULATION: - Organic - Met a need shared across professionals across all levels. NEXT STEPS: - I hope to collaborate with others to compose a Data Science course curriculum for K-12 students - In the meantime, I’ll be working on general educational resources A high tide rises all boats and builds a meaningful network of collaborative ships!

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About a year ago, I moved from leading a Science team at Amazon to a Product Manager role in Pinterest Ads. Colleagues, friends, and recruiters all ask me the same question: “Why the transition?” My answer: the choice wasn’t between Science (manager) and Product (IC). It was to optimize for learning the skills and experiences I believe will be most relevant and impactful in the coming years. I won’t add another “AI is changing everything” LinkedIn post. It’s obvious AI is already accelerating the customer signal → roadmap → product cycle. The boundaries between Engineering, Science, and Product are blurring: Product will become more full-stack and ship code directly, and Engineering/Science will be more directly customer-facing—hearing feedback and use cases firsthand. In that world, a “dangerous person” means having both the technical depth to dive deep into a problem, and the functional breadth for end-to-end execution to deliver results for customers. One could argue this has always been the case, but as AI lowers entry barriers across domains and complements one’s existing expertise, the technical depth + functional breadth combination increasingly feels like table stakes. So I evaluated what I already know—and what I need to learn. What I know: I was fortunate to rotate across different business domains at Amazon and learn how economics/ML/data science are applied to ads measurement, public-policy for AI infrastructure, measuring and improving customer experience (e.g., subscription and omnichannel). Having built and managed a science team, I also became familiar with different operating models for the science function: embedded within product teams or as centralized teams. What I want to learn: 1. Owning customer outcomes: shifting from “Is this statistically significant?” to “What should we do to improve customer experience and drive revenue?” 2. Go-to-market through cross-functional partnership: working closely with Product Marketing, Sales, Legal, Ops, and others to drive strategy and execution. Instead of optimizing to become a “better manager,” “better scientist,” or “better PM” in isolation, I’m optimizing for building my own combination of skills—and finding the right opportunity fit to solve meaningful problems and deliver results. It’s been a rewarding year at Pinterest: more often than not, I’m in the driver’s seat for outcomes for customers and stakeholders, rather than just providing “data-driven recommendations.” I’ve learned a lot about the full cycle from ideas (inputs) to customer and business outcomes (outputs), and I am excited about what’s more to come on this journey!

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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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Inference at scale is evolving. The future is composable systems where offline batch, online batch (near-realtime), and realtime traffic can all be served from a single, unified inference endpoint for most use-cases. This simplifies your architecture, reduces operational overhead, and increases throughput. Our new tutorial series will show you how to build it step by sep. In the first installment, Erik Saarenvirta walks you through creating a scalable image classification system on GKE that can be adapted to a variety of use-cases. Leave us feedback in comments or ask questions we are happy to answer. Kent Hua Ishmeet Mehta Erik Saarenvirta https://lnkd.in/dVNiUhwE

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