DataFramed

DataCamp

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

  1. #362 How to Have a Data Science Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch

    5 hr ago

    #362 How to Have a Data Science Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch

    The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply? Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers. In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more. Links Mentioned in the Show • Chip Huyen — AI Engineering (book) • Andrew Codesmith on YouTube • Phillip Choi on YouTube • A Life Engineered on YouTube • Keras • LeetCode • Connect with Marina: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Career in Data Science in 2025 with Dawn Choo New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobileEmpower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

    48 min
  2. #361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks

    25 May

    #361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks

    Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or being hired — be paying attention to right now? Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium. In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more. Links Mentioned in the Show: Connect with Veronika: LinkedInVeronika's Substack: Think. Solve. Repeat.dbt — referenced as the origin of "analytics engineering"Open Data Science Conference (ODSC) — Veronika's recent talk on data and company politicsAmazon "two-way door" decisions — Bezos shareholder letterJessica Talisman — Veronika's recommendation for knowledge graphs and ontologiesJuan Sequeda — referenced on semantic layers and knowledge graphsCatalog & Cocktails podcast (hosted by Juan Sequeda)AI-Native Course: Intro to AI for WorkRelated Episode: Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at Duolingo New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

    49 min
  3. #360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants

    18 May

    #360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants

    Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day? Reid Blackman is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast. In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more. Links Mentioned in the Show: • The Ethical Nightmare Challenge by Reid Blackman • Ethical Machines by Reid Blackman • Ethical Machines podcast • Claude Code • Connect with Reid: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: #350 How to Make Hard Choices in AI with Atay Kozlovski New to DataCamp? Learn on the go using the DataCamp mobile app.Empower your business with world-class data and AI skills with DataCamp for business.

    57 min
  4. #359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota

    12 May

    #359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota

    Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association. In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more. Links Mentioned in the Show: Artificial Intimacy by Sherry Turkle Being You: A New Science of Consciousness by Anil SethLiberation Day: Stories by George SaundersHard Fork podcast (NYT)Connect with ValerieAI-Native Course: Intro to AI for WorkRelated Episode: #342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at Wharton New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

    44 min
  5. #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

    4 May

    #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

    Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit? Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents. In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more. Links Mentioned in the Show: • Claude Code (Anthropic) • Yutori • Waymo • Apple Project Titan • DeepSeek-V3 Technical Report • Kimi K2 Technical Report • Connect with Ruslan: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

    58 min
  6. #357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs

    27 Apr

    #357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs

    The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one? Ben Zweig is the CEO and Co-Founder of Revelio Labs, where he leads the development of a universal HR database built on over a billion public employment profiles and more than 5 billion job postings. He holds a PhD in Economics from the CUNY Graduate Center and teaches Data Science and The Future of Work at NYU Stern. Before founding Revelio Labs, he managed Workforce Analytics projects in the IBM Chief Analytics Office and worked as a data scientist at an emerging-markets hedge fund. He is the author of Job Architecture: Building a Workforce Intelligence Taxonomy. In the episode, Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks not skills, building universal taxonomies from billions of job postings, which data careers resist AI, advice for hiring data talent, when traditional NLP beats LLMs, and much more. Links Mentioned in the Show: Ben's book — Job Architecture: Building a Workforce Intelligence TaxonomyRevelio LabsO*NET — the US government occupational taxonomy Ben critiquesBaruch Lev — The End of AccountingHaskel & Westlake — Capitalism Without CapitalJustified Posteriors podcast (Andrey Fradkin & Seth Benzell)Connect with Ben: LinkedInAI-Native Course: Intro to AI for WorkRelated Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

    58 min
  7. #356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple

    20 Apr

    #356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple

    Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on? Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there. In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more. Links Mentioned in the Show: Forecasting: Principles and Practice (Rob Hyndman)NixtlaskforecastProphetConnect with RamiAI-Native Course: Intro to AI for WorkRelated Episode: Developing Better Predictive Models with Graph Transformers New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

    54 min
  8. #355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft

    13 Apr

    #355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft

    Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents actually reduce on-call pain without taking away needed control? Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\n Previously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\n Shireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus. In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more. Links Mentioned in the Show: Microsoft FabricAzure Cosmos DBWhat is Azure SQL Database?Connect with ShireeshAI-Native Course: Intro to AI for WorkRelated Episode: Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at MicrosoftExplore AI-Native Learning on DataCamp New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

    53 min
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About

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

Information

  • Creator
    DataCamp
  • Years Active
    2018 - 2026
  • Episodes
    300
  • Rating
    Explicit
  • Copyright
    © 2022 DataFramed
  • Show Website
    DataFramed

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