Data Neighbor Podcast

• Ep42: What is Vibe Analytics and How to Get Your Company Ready for It?

  In this episode of the Data Neighbor Podcast, we sit down with Lei Tang, co-founder and CTO of Fabi AI, to explore the messy reality of data quality, the limits of self-serve BI, and why Vibe Analytics might be the shift organizations need. With experience leading data science at Lyft, Walmart Labs, and Clari, Lei brings grounded, first-hand insights into how modern data teams can thrive even when their data is anything but clean.You’ll learn:- Why “perfect data” is a myth and what to do instead- How AI-native BI changes the self-serve equation- The challenges and promise of Vibe Analytics- Why critical thinking, not SQL, is your most valuable skill- The case for AI-driven semantic layers over manual curation- How AI agents might evolve into collaborative teammates- Real risks of AI hallucinations and how to build guardrailsIf you’ve ever dealt with stakeholder overload, a graveyard of unused dashboards, or felt stuck waiting on a “single source of truth” project to finish, this one’s for you. We get real about trade-offs, show how AI can amplify impact (not replace you), and dive into what the future of analytics workflows might actually look like.Connect with Lei: https://www.linkedin.com/in/lei-tang-ai/Connect with Hai, Sravya, and Shane (let us know which platform 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 #vibeanalytics #fabi #selfservebi #aiinanalytics #dataquality #dataengineering #dataops #aibi #dataneighborpodcast #aiproducts #dataworkflows #analyticsleadership #futureofanalytics

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• Ep41: What's an AI Research Engineer??

  AI is evolving faster than ever, and the people keeping up with it are the AI Research Engineers. In this episode of the Data Neighbor Podcast, we sit down with Sandi Besen, AI Research Engineer at IBM Research, to unpack what it actually means to live and work on the bleeding edge of AI.Sandi shares what it takes to move from model demos to real systems, why research engineering is becoming one of the most critical jobs in tech, and how she prototypes, evaluates, and ships new agent frameworks at record speed.Connect with the team (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Sandi: https://www.linkedin.com/in/sandibesen/In this episode, you’ll learn about:-What an AI Research Engineer actually does day-to-day-How research engineering bridges AI research and production-Why requirements frameworks help agents stay reliable-The trade-offs between low-code and pro-code approaches-How evals and observability are evolving for agent systems-The human side of working at the frontier of AI#aipodcast #airesearch #ibmresearch #aiagents #agentframeworks #llm #datascience #mlengineering #automation #aidevelopment #beeai #aiproducts #researchengineering #dataneighbor #aifuture #ibm

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• Ep40: Why Most AI Agents Fail - And How to Build Agents You Can Count On

  AI is moving fast, but reliable agents are still rare. In this Data Neighbor Podcast, we sit down with Jigyasa Grover, ML Engineer at Uber, author of Sculpting Data for ML: The first act of Machine Learning, and member of Google’s ML Advisory Board, to unpack why most AI agents fail and what it really takes to build ones you can count on.Jigyasa shares how to design, evaluate, and secure reliable agent systems - from memory management and adversarial testing to using human judgment without slowing down innovation.Connect with the team (tell us YouTube sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Jigyasa: https://www.linkedin.com/in/jigyasa-grover/In this episode, Jigyasa explains how agents evolve beyond simple workflows into autonomous systems, why evals are at the heart of reliable AI, and how developers can prevent silent failures through better design, testing, and observability.You'll learn about:-Why most AI agents fail and how to engineer reliability from day one-Workflow agents vs LLM-based agents-How evals, memory hygiene, and adversarial testing improve reliability-When to use traditional ML instead of LLMs-Designing for human judgment, security, and recovery in agent systems#aipodcast #aiagents #aidevelopment #aiengineering #llm #mlops #datascience #agentdesign #workflowagents #memory #evaluation #productstrategy #aiproductmanagement #autonomousagents #aiethics #aideployments #reliableai #dataneighbor #jigyasagrover #agenticai

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• Ep39: How to 10X Data Work with HEX Agentic AI

  AI is reshaping data and analytics, moving from brittle dashboards to agentic, conversational workflows. In this Data Neighbor Podcast, we sit down with Barry McCardel, CEO & Co-founder of Hex, to unpack how agentic analytics, natural-language querying, and semantic modeling are changing how data teams (and the whole business) make decisions. Connect with Shane, Sravya, and Hai (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Barry: https://www.linkedin.com/in/barrymccardel/In this episode, Barry shares how Hex evolved beyond notebooks into a self-serve BI + AI agent platform, why PMF is a moving target in AI, and how great data teams are shifting from ticket queues to curation, governance, and partnership.You'll learn about:- Agentic analytics in practice: from “chat with my data” to explainable, reproducible workflows (thinking traces, SQL visibility, versioned projects).- How semantic models (Hex, Snowflake, dbt, Cube) unlock trusted self-serve BI.- How to find PMF in AI: sustaining product-market fit when model capabilities shift weekly.- What is Data team 2.0: moving repetitive “pull a number” requests to agents so humans focus on curation, modeling, experimentation, and strategy.- How to ship rigor at speed: why transparency, lineage, and observability matter for trust—not just accuracy.#aiproductmanagement #agenticanalytics #conversationalbi #datateams #selfserveBI #semanticlayer #dbt #snowflake #dataapps #llm #aiagents #mlops #productstrategy #dataneighbor #hextech #hex #datascience #ai

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• Ep38: How to Land a Machine Learning Job Today

  Is the future of Machine Learning Engineer (MLE) jobs secure in the age of AI? Umang Chaudhary, an ML Engineer at TikTok (formerly Amazon), dives deep into this pressing question and shares his invaluable insights on navigating the rapidly evolving ML landscape. In this episode, Umang recounts his unique journey from web development to a thriving MLE career, the challenges of ML interview prep, and why he's now dedicated to guiding aspiring ML professionals.Discover how Umang leverages cutting-edge AI tools like Gemini and Grok in his daily workflow and for interview preparation, offering a fresh perspective on productivity and learning. Learn about the common fears and questions his mentees face regarding AI's impact on job security and how to differentiate between "real-world" ML skills and those needed to ace interviews. This episode is a must-watch for anyone looking to break into or advance in the ML field, offering a blend of career guidance, practical tips, and a compelling look into the future of AI.In this episode, you will learn:* The evolving role of AI and LLMs in daily ML workflows, from solution building to enhanced productivity.* How Umang leverages AI tools like Gemini Pro and Grok for efficient coding, document analysis, and comprehensive ML system design interview preparation.* Umang's unique journey, transitioning from web development to a Machine Learning Engineer role at Amazon and then TikTok.* Current concerns from aspiring ML professionals about AI's impact on the future of MLE jobs and Umang's perspective on career longevity.* Inspiring stories of individuals making unconventional transitions into ML engineering roles, including web developers, data analysts, and product managers.* A four-step plan to effectively break down and master Machine Learning interview preparation (ML fundamentals, ML design, ML system design, ML coding).* The critical importance of patience and a strategic "numbers game" approach to landing an ML job in today's competitive market.Connect with Umang:https://www.linkedin.com/in/mlwithumang/https://www.instagram.com/umangabroad/https://www.instagram.com/ml.with.umang/Connect with Hai, Sravya, and Shane (let us know which platform 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/#MLEngineer #MachineLearning #AIJobs #LLM #AICareers #MLCareerGuidance #MLInterviewPrep #TikTok #Amazon #DataScience #TechCareers #CareerTransition #Grok #ChatGPT #Gemini #Entrepreneurship #MachineLearningEngineer #AIInnovation #DataNeighborPodcast

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