Data Neighbor Podcast

• Ep 24: She was the First Data Hire at a Startup - 3 Things That Shocked Her

  What does it really take to be the first data hire at a startup?In this episode of the Data Neighbor Podcast, we dive deep with Jessie Li - former Head of Data at Descript and ex-Meta product data science leader - as she unpacks her raw, insightful journey navigating the unpredictable world of early-stage startups. From leaving Big Tech for a less-known startup to building a full-stack data function from scratch, Jessie shares the highs, lows, and the very real lessons learned along the way.We cover: - What shocked Jessie most on Day 1 as Descript’s first data hire. - Why bringing Big Tech best practices into a startup doesn’t always work. - How to build data foundations, insights functions, and culture from zero. - The push/pull model for career transitions (and how to think two jobs ahead). - Why being “too humble” in a startup can backfire - and how to avoid disorientation. - What startups really need in data hires - and how to know if you're a good fit. - The underrated importance of AI in data workflows today.Whether you're contemplating a startup move or want an honest look at what it takes to own and scale data in a high-growth environment, this is an unfiltered and inspiring conversation packed with lessons for data professionals at every level.Connect with Jessie: https://www.linkedin.com/in/jessieljy/Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #Startups #JessieLi #FirstDataHire #Descript #DataCulture #DataEngineering #AIInData #CareerGrowth #BigTechVsStartup #DataNeighborPodcast #DataLeadership #IntentionalLearning #StartupHiring #WomenInData #DataDriven

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• Ep23: What if AI Teaches You AI

  In this episode of Data Neighbor, we dive deep into the rapidly evolving landscape of education driven by AI. Jess Haberman, Director of Product Content at Anaconda and former O'Reilly editor, shares groundbreaking insights on how generative AI tools are revolutionizing the way we learn Python, data science, data engineering, and beyond - making learning faster and more accessible than ever before.​ AI is even teaching us how to speak with ai, how to build ai, and how to learn how ai works.Key Takeaways:-How AI enables learners to master Python and technical skills significantly faster.-The future of traditional education, online courses, and tech bootcamps.-Critical soft skills you need to thrive in the AI-powered job market.-Why AI-assisted learning is now a necessity, not just an advantage.-Practical strategies to leverage generative AI for your professional development.​Connect with Jess:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/X: https://x.com/JessHabermanBluesky: https://bsky.app/profile/jesshaberman.bsky.socialWebsite: https://www.jesslhaberman.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feHai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8c

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• Ep22: What Is Marketing Science (and How It Stops Wasted Spend)

  Ever wondered who’s behind the ads that actually make you buy stuff—and how companies waste millions on the ones that don’t? In this mind-opening episode of the Data Neighbor Podcast, we dive into the world of marketing science with Agastya Komarraju, Global Head of Product and Science for Growth & Funnel at Amazon. Agastya has held leadership roles at Walmart and Nielsen, and he’s here to demystify how data powers smarter marketing—and how Amazon slashed 90% of marketing spend without hurting results.- What exactly is marketing science and how does it differ from traditional data science? - Why “50% of your marketing spend is wasted”—and how to figure out which half. - How direct mail still outperforms digital ads (yes, seriously). - The five pillars of marketing science—from experimentation to attribution. - How AI and GenAI are transforming marketing from creative to targeting. - Real-world wins: The genius experiments that won Agastya and his teams top industry awards.Whether you're a marketing leader, data scientist, or just someone who wants to understand why you keep seeing that ad for a coffee mug you Googled once, this episode is packed with insights on consumer behavior, optimization, and the science behind persuasion. Learn how the biggest brands in the world are applying data—and what that means for your role, your budget, and your career.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Agastya: https://www.linkedin.com/in/agastya-kumar-komarraju-95b60446/Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/Sravya: https://www.linkedin.com/in/sravyamadipalli/Shane: https://www.linkedin.com/in/shaneausleybutler/

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• Ep21: From Zero to GenAI - What ML Engineers & Data Scientists Need to Learn

  How is AI quietly reshaping your everyday workflow—whether you notice it or not? In this episode of the Data Neighbor Podcast, we explore how AI is being embedded directly into the tools you already use, with real-world examples from Adobe Acrobat.Our guest, Nikhil Pentapalli, is a Senior Machine Learning Engineer at Adobe, where he works on embedding GenAI into Acrobat to transform how people interact with documents. From AI assistants that can summarize and answer questions across multiple PDFs, to intelligent restructuring of scanned documents for mobile, we unpack what it really looks like to bring AI into production—and why it’s harder than it seems.We cover:- How AI is becoming invisible infrastructure in everyday workflows. - Why integrating AI into documents is harder than you'd think. - What makes PDFs complex, and how GenAI is making them smarter. - Building GenAI products: from prompt engineering to memory optimization. - Advice for breaking into GenAI, and what ML engineers need to know now. - Traditional ML vs LLMs: where to use which, and how to manage costs.Whether you're building AI tools, thinking about career moves, or just curious about what it takes to ship real GenAI products, this episode is loaded with insights and practical advice.Connect with Hai, Sravya, and Shane:- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#genai #aiworkflow #documentai #rag #retrievalaugmentedgeneration #acrobatai #llm #promptengineering #mlengineer #aiintegration #realworldai #aiproducts #datascience #dataneighborpodcast

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• Ep20: How to Build a Data Analytics App in Minutes (Replit, Cursor, Bolt, V0, Lovable)

  AI is transforming how products are built—and with new AI prototyping tools like Replit, Cursor, V0, Bolt, and Lovable, it’s never been easier to go from idea to working app in minutes. In this episode of the Data Neighbor Podcast, we’re joined by Colin Matthews, founder of Tech for Product, to explore how product managers, data scientists, and engineers can build faster using AI.Colin teaches two of the top AI and technical fluency courses on Maven, writes the Tech for Product Substack, and helps builders leverage tools like Replit and GPT to build and test real products—fast.📚 Courses from Colin:AI Prototyping for Product Managers: https://maven.com/tech-for-product/ai-prototyping-for-product-managersTechnical Foundations for Product Managers: https://maven.com/tech-for-product/tech-fundamentals💼 Follow Colin Matthews:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/Substack: https://blog.techforproduct.com/Website: https://techforproduct.com/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):Hai Guan: https://linkedin.openinapp.co/4qi1rSravya Madipalli: https://linkedin.openinapp.co/9be8cShane Butler: https://linkedin.openinapp.co/b02feIn this episode, we cover:- How to prototype real apps with AI (Replit, Cursor, Lovable, Bolt, V0)- How AI is changing the role of product managers and engineers- Replit demo: How we built a working data analysis app with zero code- AI vs traditional product development: What’s faster?- When these tools are actually production-ready- How product teams can use AI to validate ideas early- AI tools for data science, analytics, and product strategy- Best practices for debugging and building with AI agents- The real role of product-market fit in AI-first prototyping- How to avoid common mistakes when using AI tools- Colin’s advice for staying up to date with the fast-moving AI stackIf you work in AI, product, data science, machine learning, or tech strategy—or you’re a founder trying to get your idea off the ground—this episode is a goldmine of practical insights. From MVPs to experimentation platforms, we explore how AI is changing who gets to build, how fast they can do it, and what the future of product development looks like.#AI #AIPrototyping #ProductManagement #ProductDevelopment #DataScience #MachineLearning #AIApps #Replit #Cursor #V0 #Bolt #Lovable #AIForStartups #NoCodeAI #GPTApps #TechForProduct #MavenCourses #AICodingTools #BuildWithAI #AIProductStrategy #AIforPMs #RapidPrototyping #TechnicalFluency #LLMTools #AIStack #AIEngineering #DataTools #AIUX #Streamlit #OpenAI #AIAppDemo

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