Data Neighbor Podcast

• Ep44: The Future of AI Teams - Research, Product, and Domain Expertise

  In this episode of the Data Neighbor Podcast, we sit down with Shelby Heinecke, PhD, Senior AI Research Manager at Salesforce, to break down what modern AI teams actually look like and how enterprise AI gets built in practice.Shelby shares how her team moves research into production, why small crisp problem definitions outperform ambitious abstractions, and how evaluation before development has become a non negotiable part of the workflow.We also talk about the shifting shape of AI teams, the rising importance of domain experts, and why interdisciplinary collaboration is quickly becoming the core of the field.If you want an inside look at how leading AI orgs actually operate, this is the episode.Connect with the team (tell us which platform sent you!):- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Shane Butler: https://linkedin.openinapp.co/b02fe - Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Shelby:https://www.linkedin.com/in/shelby-heinecke/In this episode, you’ll learn:- How enterprise AI research teams actually set direction- Why crisp scope and early evaluation decide which projects reach production- What makes interdisciplinary collaboration essential for AI success- How small models and agents are being deployed across Salesforce- What skills matter most for the next generation of AI roles- Why embodied agents may represent the next major leap in AI#aipodcast #airesearch #salesforce #aiteams #aiproducts #llm #datascience #mlengineering #aidevelopment #agents #embodiedai #dataneighbor #aifuture

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• Ep43: Building Women in Data: Sadie’s Playbook for Starting Movements

  Join us for an inspiring conversation with Sadie St Lawrence, founder and CEO of Women in Data, and the Human Machine Collaboration Institute! Sadie shares her incredible journey from piano and neuroscience to pioneering a global movement empowering tens of thousands of women in data and AI. Discover her unique insights on building impactful communities, navigating career changes, and the evolving role of humans in the age of AI.In this episode, we cover:- Sadie's fascinating career trajectory, from a neuroscience lab to a data science pioneer and community builder.- The origin story of Women in Data, starting from a personal need for community to a global movement of 70,000 members across 120+ countries.- The critical role of consistency and trust in building a thriving community and achieving professional growth.- The current landscape of diversity in data careers, with eye-opening statistics and the significant impact of female leadership.- Sadie's visionary perspective on the future of work, where humans become "orchestra conductors" in a world augmented by AI.- The mission of the Human Machine Collaboration Institute (HMCI) in tackling fundamental questions about humanity, emotion, and consciousness in the AI era.- Practical advice on cultivating curiosity, breaking patterns, and leveraging your innate desire to learn for career advancement and personal fulfillment.Whether you're looking to start a community, advance your career in data, or curious about the philosophical implications of AI, Sadie's story and insights offer invaluable lessons. Tune in to understand why consistency, community, and curiosity are your greatest assets in the rapidly changing world of technology.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 #AI #WomenInData #CareerAdvice #TechLeadership #CommunityBuilding #HumanMachineCollaboration #Curiosity #DiversityInTech #Neuroscience #Consciousness #FutureOfWork #DataCareers #STEM #ProfessionalDevelopment #Podcast #DataNeighbor

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• 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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