Data Neighbor Podcast

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

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

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

Unlock the secrets to building a future-proof data organization that thrives on impact, not just effort. Join us as we sit down with Manoj Mohan, former Engineering Leader of Data and AI Platforms at Intuit, and a seasoned leader from Meta, Cloudera, and Apple. Manoj shares his deep insights from two decades in the data, ML, and AI space, offering pragmatic strategies for long-term success.In this episode, you’ll discover:- Hard-won lessons from early data warehouse failures and the critical role of humility and scalability in data projects.- Why embracing a "platform as a product" mindset for data engineering is essential for long-term efficiency and avoiding KPI chaos.- Manoj Mohan's powerful "3 Gs" framework (Grounded, Guarded, Governed) for deploying Large Language Models (LLMs) responsibly and effectively within the enterprise, comparing them to high-speed Formula One cars that need robust guardrails.- A visionary outlook on what a future-proof data organization might look like by 2030, where AI-driven insights are seamlessly accessible to every employee.- Practical advice for startups on balancing speed with sustainable data infrastructure, ensuring foundational blocks are built alongside product innovation.- Key principles for data leaders, including the importance of continuous learning, unlearning, and focusing on problem-solving over tools.Whether you're a data engineer, an AI enthusiast, a data leader, or navigating data challenges in a startup, this episode is packed with invaluable wisdom to help you build resilient, scalable, and impactful data systems.

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/#DataEngineering #AIPlatforms #LLMs #DataStrategy #Scalability #DataGovernance #ResponsibleAI #PlatformAsAProduct #FutureOfData #DataOrganization #StartupData #EnterpriseAI #DataLeadership #MLEngineering #DataManagement #ManojMohan #DataNeighborPodcast #TechLeadership

AI is changing product management, from how teams prototype to how they measure success. In this episode of the Data Neighbor Podcast, we’re joined by Aman Khan, Head of Product at Arize AI (LLM evaluation & observability). Aman breaks down the three emerging AI PM archetypes (AI-native PM, AI platform PM, and AI-powered PM), how to move from “vibe coding” to eval-driven development (EDD), and why aligning evals to business outcomes matters more than any single accuracy score. He also shares hard-won tactics for handling subjectivity in LLM outputs, setting user expectations in UX, and deciding when rigor can (and can’t) slow down speed. In this episode, you’ll learn:-The three ways AI shows up in PM work—and how those roles are converging.-A practical ladder from “vibe checks” to EDD (evals in dev & production), including LLM-as-a-judge and when to trust it.-How to tie evals to business metrics (trust, value, speed) and resolve “good eval, bad outcome” conflicts.-UX patterns for long-running agent tasks (progress, ETAs, checkpoints) that preserve trust.-Where AI coding tools help most (and least) across engineers, PMs, and data teams.Connect with Aman Khan:LinkedIn: https://www.linkedin.com/in/amanberkeley/🌐 Website: https://amank.ai🏢 Arize AI: https://arize.com/ Arize AIConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r

#aiproductmanagement #aievals #llmobservability #productmanagement #datascience #mlops #aiagents #evaluation #productstrategy #dataneighbor #arizeai #llms

Are you overwhelmed by ad-hoc data questions? Ever wondered how to automate business intelligence with AI? Join us as we sit down with Lohitaksh Yogi, a seasoned AI product leader from companies like ServiceNow and Adobe, to explore the exciting world of AI data agents and Natural Language Business Intelligence (NLBI). Lohit shares his journey from early machine learning chatbots to cutting-edge LLM-powered conversational AI, offering invaluable insights into building and deploying these transformative systems.In this episode, you will learn:- The evolution of chatbots: Understanding the limitations of early rule-based systems vs. the powerful context-awareness of LLMs.- The vision for Natural Language Business Intelligence (NLBI): How close we are to asking a chatbot natural language questions and getting instant insights from our data.- Key challenges in AI deployment: Navigating schema ambiguities, data inconsistencies, and the critical issue of AI hallucinations.- Strategies for building an effective AI data agent: From designing intuitive user experiences (UX) to implementing robust error handling and feedback loops.- The paramount importance of data governance: Protecting sensitive information and ensuring data privacy when leveraging AI for internal data analysis.- Why internal beta testing is crucial: Breaking your system internally before exposing it to external stakeholders to build trust and ensure accuracy.- The right mindset for AI adoption: Viewing AI as an investment for long-term productivity gains, not a quick fix, and understanding its rapid evolution.Whether you're a data professional looking to boost productivity, a business leader seeking to automate data requests, or just curious about the future of AI in the enterprise, this episode provides actionable strategies and a realistic outlook on deploying AI data agents.

Connect with Lohitaksh: https://www.linkedin.com/in/lohitakshyogi/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/#Chatbots #ConversationalAI #AIDataAgents #NaturalLanguageBI #LLM #LargeLanguageModels #DataGovernance #AIHallucination #DataAnalytics #DataScience #MachineLearning #ProductLeadership #AIStrategy #UserExperience #DataWorkflow #AIPodcast #TechInsights #DataProduct #EnterpriseAI

What truly defines a good data scientist, and how can you excel in this rapidly evolving field? Join us as we sit down with Siddharth Ranganathan, Director of Data Science at Microsoft, to uncover practical insights on navigating data science careers, balancing rigor with business needs, and the transformative impact of AI. Siddharth shares invaluable lessons from his extensive experience, emphasizing impact over complexity and strategy over execution.In this episode, we cover:- What constitutes good data science: Focusing on decisions, impact, scientific rigor, and practicality.- Balancing speed and rigor in analysis: Strategies for delivering timely insights without compromising integrity.- Common misunderstandings about product data science: It's more than just building ML models; it's a strategic, cross-functional role.- How to become a strategic data scientist: Shifting focus from outputs to outcomes and asking better questions.- The evolving landscape of data science with AI and Gen AI: Anticipating the rise of role-based agents and the convergence of tech and business.- Identifying and avoiding common career traps for data scientists, such as staying in execution mode or over-indexing on technical depth.- Key factors directors look for in promotions: Driving impact beyond your current level, securing patrons, and clearly communicating your contributions.- The most underrated skill for a data scientist: The ability to break down complex problems and deal with ambiguity.Whether you're an aspiring data scientist, a mid-level professional looking to grow, or a leader shaping data teams, this episode offers a wealth of actionable advice to elevate your data science career and impact.
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 #ProductDataScience #AI #GenAI #LLMs #CareerGrowth #StrategicDataScientist #Microsoft #DataScienceCareer #DataSciencePromotions #DataScienceAdvice #DataScienceLeadership #ImpactOverComplexity #TradeOffs #DataNeighborPodcast

AI is fundamentally changing how we build and manage products—but agentic AI takes things to an entirely new level. In this episode of the Data Neighbor Podcast, we’re joined by Mahesh Yadav, who has built and launched AI-driven products at leading FAANG companies including Microsoft, Meta, Amazon, and Google. He’s also the creator of the popular Maven course on Agentic AI Product Management: https://maven.com/mahesh-yadav/genaipmMahesh shares firsthand insights into how the product lifecycle for AI-driven features differs from traditional development, the critical importance of robust evaluations, and how teams can practically adapt to the rapidly evolving landscape of AI. Whether you're a product manager, data scientist, engineer, or executive navigating the complexities of integrating AI into your products, this episode is your practical guide to thriving in the AI-first world.In this episode, you'll learn:- How the product lifecycle for agentic AI products differs from traditional software.- Practical frameworks for effectively evaluating AI performance and quality.- The role of subject matter experts and evaluation scientists in scaling AI products.- Strategies for staying ahead as AI reshapes traditional roles and team structures.Connect with Mahesh Yadav:🔗 LinkedIn: https://www.linkedin.com/in/initmahesh/🎓 Maven Course on Agentic AI Product Management: https://maven.com/mahesh-yadav/genaipmConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r

#ai #agenticai #productmanagement #productdevelopment #llms #aiproductmanagement #aiagents #aieval #evaluationmetrics #machinelearning #datascience #faang #productstrategy #dataanalytics #dataneighbor #businessintelligence #productleadership

Ercan Kamber, former Chief Data Officer at Angi and seasoned leader from Twitter and Microsoft, joins the Data Neighbor Podcast for a masterclass on scaling data organizations, embracing AI, and navigating C-suite challenges. As the first CXO to appear on the show, Ercan opens up about what it really means to be a CDO, the mindset shift from tech contributor to enterprise-wide leader, and how to build AI-empowered data teams that matter.In this episode, we cover:🏗️ How Ercan built Angi’s first centralized data org after multiple mergers.📈 The real meaning of “data strategy” in complex business environments.🧭 Transitioning from big tech to startup C-suite: lessons in ownership and context switching.🧠 The rise of AI agents: What AI-first and AI-forward really mean - and why it matters.⚖️ Balancing speed, cost, and precision in ML systems.📊 How to create a scalable operating system for modern data teams using Agile.👁️ Communication secrets for working with executive teams.💡 What the future of AI agents might mean for labor, startups, and society.This episode is packed with hard-earned wisdom and actionable advice, whether you’re a rising data scientist or leading data for a global enterprise. Ercan brings both vision and pragmatism - don’t miss this conversation!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/#datascience #aiagents #chiefdataofficer #dataleadership #cdorole #bigtechcareer #dataorganization #mlops #datateams #aifuture #agenticai #datastrategy #dataneighborpodcast #aiinbusiness #cdoinsights

Your data insights are worthless if no one understands them. In this episode of the Data Neighbor Podcast, we’re joined by Matt Harrison, author of Effective Pandas, Effective Visualization, and many more bestselling technical books. Matt joins us to uncover the secrets behind impactful, professional data storytelling.Learn how to transform complex data into clear, compelling narratives that resonate with stakeholders and drive action. Whether you're a data scientist, analyst, product manager, or anyone who deals with data visualization, Matt’s proven 5-step CLEAR framework will help you craft visuals that communicate with clarity, simplicity, and effectiveness.In this episode, you'll learn:* How to avoid common mistakes data professionals make when visualizing data.* Why "fancy" charts often fail and how to master simple visuals that tell better stories.* Practical tips for using color, annotations, and design principles like a pro.* How top media outlets (New York Times, The Economist) use these exact methods to captivate their audiences.Connect with Matt Harrison:📚 Website: https://www.metasnake.com🔗 LinkedIn: https://www.linkedin.com/in/panelaConnect with Shane, Sravya, and Hai (let us know YouTube sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r

#datastorytelling #datavisualization #datascience #analytics #python #matplotlib #effectivevisualization #pandas #storytellingwithdata #visualcommunication #machinelearning #datastrategy #dataskills #dataneighbor #dataanalytics #datascientist #dataengineering #businessintelligence