Data Neighbor Podcast

• Ep32: Will AI Take Your Job? A Chief Data Officer Explains

  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

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• Ep31: 5 Steps to Master Effective Visualization

  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

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• Ep30: Machine Learning with NO Tech Background? Marina’s Guide to Breaking In

  Ever wondered how someone with a political science degree ends up doing machine learning at Twitch? Meet Marina Wyss - applied scientist, blogger, YouTuber, and all-around productivity and learning expert. In this episode of the Data Neighbor Podcast, Marina shares her unconventional journey into tech, how she self-taught herself machine learning, and why her mantra of being “gratitude-driven” is her antidote to hustle culture.We dive into:- How Marina transitioned from political science and jewelry management into data science and ML.- Her self-study roadmap: From free courses to Coursera to deep technical books.- Practical frameworks for self-learning, getting promotions, and breaking into the ML industry.- Why she rejects hustle culture in favor of a gratitude-driven approach to productivity.- How she leverages AI tools like ChatGPT and Replit to accelerate learning and personal projects.- Common mistakes early learners make and how to avoid being overwhelmed.- Her take on Python vs R, what to focus on when starting ML, and why building your own projects is essential.Whether you're coming from a non-technical background, looking to break into ML, or trying to navigate learning in the age of AI, Marina’s story and advice will inspire you to take the leap - and build the skills that matter.Links Mentioned in the EpisodeMarina’s Blog: https://www.gratitudedriven.com/Books Mentioned:- Designing Machine Learning Systems by Chip Huyen- AI Engineering by Chip Huyen- Software Engineering for Data Scientists by Catherine NelsonCourses:- Machine Learning Specialization by Andrew Ng (Coursera)- Deep Learning Specialization (deeplearning.ai)- Math for Machine Learning (Three Blue One Brown on YouTube)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/#machinelearning #datascience #careertransition #gratitudedriven #selfstudy #ai #deeplearning #python #coursera #careerroadmap #productivity #chatgpt #learnML #dataeducation #DataNeighborPodcast #nontechtotext #womenintech #mlprojects

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• Ep29: Top 3 AI Security Flaws Killing Products - and How to Fix Them

  AI systems are becoming integral to nearly every digital product, but their vulnerabilities pose real and serious risks. How can companies protect their AI-powered products from security threats like prompt injection, jailbreaking, and misalignment?In this episode of the Data Neighbor Podcast, we're joined by Sander Schulhoff, CEO of Hacker Prompt and founder of Learn Prompting, to uncover critical security flaws in AI systems and practical ways to defend against them. With insights gathered from over 600,000 real-world AI exploits, Sander breaks down the three most dangerous AI security failures threatening today's products and provides actionable strategies to safeguard your systems.Connect with Sander Schulhoff:LinkedIn: https://www.linkedin.com/in/sander-schulhoff/AI Red Teaming Masterclass: https://maven.com/learn-prompting-company/ai-red-teaming-and-ai-safety-masterclassHack A Prompt: https://www.hackaprompt.com/Learn Prompting: https://learnprompting.org/Connect with Shane, Sravya, and Hai (let us know YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rYou'll learn essential techniques for securing AI systems, including how to recognize and prevent prompt injection and jailbreaking attacks, strategies for detecting misalignment early, and how to effectively leverage automated red teaming alongside human expertise. Sander also explores why security considerations must move from late-stage fixes to foundational aspects of AI model development and deployment.We discuss emerging security threats with autonomous agents, the role of government and compliance in AI security, and practical advice for teams at any stage—from startups to large enterprises—to proactively address AI security.If you're a data scientist, product leader, security professional, or executive interested in deploying secure AI systems, this episode provides critical insights and practical steps to protect your products and your users.#AIsecurity #promptinjection #jailbreaking #redteaming #aisafety #machinelearningsecurity #aiattacks #dataprotection #aivulnerabilities #automatedredteaming #agenticAI #hackaprompt #aiethics #dataneighbor

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• Ep28: 7 Steps to Building Production GenAI Apps

  Generative AI applications are transforming industries, but taking a GenAI model from prototype to production can be challenging. How can teams effectively build, evaluate, and deploy powerful generative AI systems in real-world scenarios? In this episode of the Data Neighbor Podcast, we're joined by Surabhi Bhargava, a Machine Learning Tech Lead at Adobe, to explore the step-by-step process of creating and productionizing GenAI apps, including embedding strategies, chunking techniques, retrieval-augmented generation (RAG), prompt engineering, and advanced model evaluation.In this episode, you'll learn essential insights into how to build a GenAI app, including how to select the right embeddings and chunk size, effective vector database management, and methods for robust query reformulation. Discover best practices for integrating GPT, Claude, Azure AI, and OpenAI APIs into your machine learning pipelines. Surabhi also shares critical tips on optimizing your AI prototype for user testing, identifying the ideal tech stack, and managing iterative feedback.We explore how to properly evaluate GenAI models using automated and human-in-the-loop strategies, discuss practical metrics to measure AI accuracy and performance, and reveal common pitfalls in AI application development. You'll also gain insights into personalization, user experience considerations, resource management, and understanding when not to use LLMs.If you’re a data scientist, engineer, product manager, or executive looking to deepen your understanding of generative AI and effectively move AI projects from concept to production, this episode is your ultimate guide.Connect with Surabhi Bhargava:LinkedIn: https://www.linkedin.com/in/surabhibhargava/Connect with Shane, Sravya, and Hai (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1r

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